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data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Antonis" data-follow-user-id="3806260" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" data-unfollow-user-id="3806260"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">done</span>Following</button></div></div><div class="user-stats-container"><a><div class="stat-container js-profile-followers"><p class="label">Followers</p><p class="data">71</p></div></a><a><div class="stat-container js-profile-followees" data-broccoli-component="user-info.followees-count" data-click-track="profile-expand-user-info-following"><p class="label">Following</p><p class="data">58</p></div></a><a><div class="stat-container js-profile-coauthors" data-broccoli-component="user-info.coauthors-count" data-click-track="profile-expand-user-info-coauthors"><p class="label">Co-authors</p><p class="data">26</p></div></a><span><div class="stat-container"><p class="label"><span class="js-profile-total-view-text">Public Views</span></p><p class="data"><span class="js-profile-view-count"></span></p></div></span></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span><a class="ri-more-link js-profile-ri-list-card" data-click-track="profile-user-info-primary-research-interest" data-has-card-for-ri-list="3806260">View All (6)</a></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="3806260" href="https://www.academia.edu/Documents/in/Network_Security"><div id="js-react-on-rails-context" style="display:none" 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style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Antonis Billis</h3></div><div class="js-work-strip profile--work_container" data-work-id="115058482"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" 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class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115058482/%CE%9F%CE%B9_%CF%88%CE%B7%CF%86%CE%B9%CE%B1%CE%BA%CE%BF%CE%AF_%CE%B2%CE%B9%CE%BF%CE%B4%CE%B5%CE%AF%CE%BA%CF%84%CE%B5%CF%82_%CF%89%CF%82_%CE%BF%CE%B9%CE%BA%CE%BF%CE%BB%CE%BF%CE%B3%CE%B9%CE%BA%CE%AC_%CE%AD%CE%B3%CE%BA%CF%85%CF%81%CE%B1_%CE%BC%CE%AD%CF%83%CE%B1_%CE%B1%CF%80%CE%BF%CE%BC%CE%B1%CE%BA%CF%81%CF%85%CF%83%CE%BC%CE%AD%CE%BD%CE%B7%CF%82_%CE%BA%CE%B1%CE%B9_%CE%BC%CE%B1%CE%BA%CF%81%CF%8C%CF%87%CF%81%CE%BF%CE%BD%CE%B7%CF%82_%CE%B5%CE%BA%CF%84%CE%AF%CE%BC%CE%B7%CF%83%CE%B7%CF%82_%CF%84%CE%B7%CF%82_%CF%85%CE%B3%CE%B5%CE%AF%CE%B1%CF%82_%CF%84%CF%89%CE%BD_%CE%B1%CF%84%CF%8C%CE%BC%CF%89%CE%BD_%CF%84%CF%81%CE%AF%CF%84%CE%B7%CF%82_%CE%B7%CE%BB%CE%B9%CE%BA%CE%AF%CE%B1%CF%82">Οι ψηφιακοί βιοδείκτες ως οικολογικά έγκυρα μέσα απομακρυσμένης και μακρόχρονης εκτίμησης της υγείας των ατόμων τρίτης ηλικίας</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115058482"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115058482"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115058482; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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src="https://attachments.academia-assets.com/111577558/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115058479/Scalable_real_time_health_data_sensing_and_analysis_enabling_collaborative_care_delivery">Scalable real‑time health data sensing and analysis enabling collaborative care delivery</a></div><div class="wp-workCard_item"><span>Zenodo (CERN European Organization for Nuclear Research)</span><span>, Jun 20, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="01358e10f925ec9c9fe121a9c5553847" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111577558,"asset_id":115058479,"asset_type":"Work","button_location":"profile"}" 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thumbnail of Can Artificial Intelligence Enable the Transition to Electric Ambulances?" class="work-thumbnail" src="https://attachments.academia-assets.com/111577566/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115058478/Can_Artificial_Intelligence_Enable_the_Transition_to_Electric_Ambulances">Can Artificial Intelligence Enable the Transition to Electric Ambulances?</a></div><div class="wp-workCard_item"><span>IOS Press eBooks</span><span>, May 25, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="995b7ad8a4ff53ed01d219d3aa4450fa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="115058477"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115058477/Older_adults_healthcare_needs_and_expectations_of_a_developing_digital_platform_to_enhance_supportive_care_post_treatment_Co_creation_across_four_countries"><img alt="Research paper thumbnail of Older adults’ healthcare needs and expectations of a developing digital platform to enhance supportive care post treatment: Co-creation across four countries" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115058477/Older_adults_healthcare_needs_and_expectations_of_a_developing_digital_platform_to_enhance_supportive_care_post_treatment_Co_creation_across_four_countries">Older adults’ healthcare needs and expectations of a developing digital platform to enhance supportive care post treatment: Co-creation across four countries</a></div><div class="wp-workCard_item"><span>Journal of Geriatric Oncology</span><span>, Dec 1, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115058477"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115058477"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115058477; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="115058476"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115058476/A_multinational_investigation_of_healthcare_needs_preferences_and_expectations_in_supportive_cancer_care_co_creating_the_LifeChamps_digital_platform"><img alt="Research paper thumbnail of A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform" class="work-thumbnail" src="https://attachments.academia-assets.com/111577554/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115058476/A_multinational_investigation_of_healthcare_needs_preferences_and_expectations_in_supportive_cancer_care_co_creating_the_LifeChamps_digital_platform">A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform</a></div><div class="wp-workCard_item"><span>Journal of Cancer Survivorship</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose This study is to evaluate healthcare needs, preferences, and expectations in supportive c...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose This study is to evaluate healthcare needs, preferences, and expectations in supportive cancer care as perceived by cancer survivors, family caregivers, and healthcare professionals. Methods Key stakeholders consisted of cancer survivors diagnosed with breast cancer, prostate cancer, or melanoma; adult family caregivers; and healthcare professionals involved in oncology. Recruitment was via several routes, and data were collected via either online surveys or telephone interviews in Greece, Spain, Sweden, and the UK. Framework analysis was applied to the dataset. Results One hundred and fifty-five stakeholders participated: 70 cancer survivors, 23 family caregivers, and 62 healthcare professionals (13 clinical roles). Cancer survivors and family caregivers’ needs included information and support on practical/daily living, as frustration was apparent with the lack of follow-up services. Healthcare professionals agreed on a multidisciplinary health service with a “focus on the ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5cb578d058f534e4d55b6063459f19cb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111577554,"asset_id":115058476,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111577554/download_file?st=MTczMjc3OTEwNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115058476"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115058476"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115058476; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=115058476]").text(description); $(".js-view-count[data-work-id=115058476]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 115058476; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='115058476']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 115058476, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5cb578d058f534e4d55b6063459f19cb" } } $('.js-work-strip[data-work-id=115058476]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":115058476,"title":"A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform","translated_title":"","metadata":{"abstract":"Purpose This study is to evaluate healthcare needs, preferences, and expectations in supportive cancer care as perceived by cancer survivors, family caregivers, and healthcare professionals. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="108825195"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/108825195/Trends_in_ablation_procedures_in_Greece_over_the_2008_2018_period_Results_from_the_Hellenic_Cardiology_Society_Ablation_Registry"><img alt="Research paper thumbnail of Trends in ablation procedures in Greece over the 2008-2018 period: Results from the Hellenic Cardiology Society Ablation Registry" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/108825195/Trends_in_ablation_procedures_in_Greece_over_the_2008_2018_period_Results_from_the_Hellenic_Cardiology_Society_Ablation_Registry">Trends in ablation procedures in Greece over the 2008-2018 period: Results from the Hellenic Cardiology Society Ablation Registry</a></div><div class="wp-workCard_item"><span>Hellenic Journal of Cardiology</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">BACKGROUND In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">BACKGROUND In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society of Cardiology was created. This online database allowed electrophysiologists around the country to input data for all performed ablation procedures. The aim of this study is to provide a thorough report and interpretation of the data submitted to the registry between 2008 and 2018. METHODS In 2008, a total of 27 centers/medical teams in 24 hospitals were licensed to perform RFA in Greece. By 2018, the number had risen to 31. Each center was tasked with inserting their own data into the registry, which included patient demographics (anonymized), type of procedure and technique, complications, and outcomes. RESULTS A total of 18587 procedures in 17900 patients were recorded in the period of 2008-2018. By 2018, slightly more than 70% of procedures were performed in 7 high-volume centers (&gt;100 cases/year). The most common procedure since 2014 was atrial fibrillation ablation, followed by atrioventricular nodal reentry tachycardia ablation. Complication rates were low, and success rates remained high, whereas the 6-month relapse rates declined steadily. CONCLUSION This online RFA registry has proved that ablation procedures in Greece have reached a very high standard, with results and complication rates comparable to European and American standards. Ablation procedures for atrial fibrillation are increasing constantly, with it being the most common intervention over the last 6-year period, although the absolute number of procedures still remains low, compared to other European countries.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="108825195"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="108825195"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 108825195; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=108825195]").text(description); $(".js-view-count[data-work-id=108825195]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 108825195; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='108825195']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 108825195, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=108825195]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":108825195,"title":"Trends in ablation procedures in Greece over the 2008-2018 period: Results from the Hellenic Cardiology Society Ablation Registry","translated_title":"","metadata":{"abstract":"BACKGROUND In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society of Cardiology was created. This online database allowed electrophysiologists around the country to input data for all performed ablation procedures. The aim of this study is to provide a thorough report and interpretation of the data submitted to the registry between 2008 and 2018. METHODS In 2008, a total of 27 centers/medical teams in 24 hospitals were licensed to perform RFA in Greece. By 2018, the number had risen to 31. Each center was tasked with inserting their own data into the registry, which included patient demographics (anonymized), type of procedure and technique, complications, and outcomes. RESULTS A total of 18587 procedures in 17900 patients were recorded in the period of 2008-2018. By 2018, slightly more than 70% of procedures were performed in 7 high-volume centers (\u0026gt;100 cases/year). The most common procedure since 2014 was atrial fibrillation ablation, followed by atrioventricular nodal reentry tachycardia ablation. Complication rates were low, and success rates remained high, whereas the 6-month relapse rates declined steadily. CONCLUSION This online RFA registry has proved that ablation procedures in Greece have reached a very high standard, with results and complication rates comparable to European and American standards. Ablation procedures for atrial fibrillation are increasing constantly, with it being the most common intervention over the last 6-year period, although the absolute number of procedures still remains low, compared to other European countries.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Hellenic Journal of Cardiology"},"translated_abstract":"BACKGROUND In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society of Cardiology was created. This online database allowed electrophysiologists around the country to input data for all performed ablation procedures. The aim of this study is to provide a thorough report and interpretation of the data submitted to the registry between 2008 and 2018. METHODS In 2008, a total of 27 centers/medical teams in 24 hospitals were licensed to perform RFA in Greece. By 2018, the number had risen to 31. Each center was tasked with inserting their own data into the registry, which included patient demographics (anonymized), type of procedure and technique, complications, and outcomes. RESULTS A total of 18587 procedures in 17900 patients were recorded in the period of 2008-2018. By 2018, slightly more than 70% of procedures were performed in 7 high-volume centers (\u0026gt;100 cases/year). The most common procedure since 2014 was atrial fibrillation ablation, followed by atrioventricular nodal reentry tachycardia ablation. Complication rates were low, and success rates remained high, whereas the 6-month relapse rates declined steadily. CONCLUSION This online RFA registry has proved that ablation procedures in Greece have reached a very high standard, with results and complication rates comparable to European and American standards. Ablation procedures for atrial fibrillation are increasing constantly, with it being the most common intervention over the last 6-year period, although the absolute number of procedures still remains low, compared to other European countries.","internal_url":"https://www.academia.edu/108825195/Trends_in_ablation_procedures_in_Greece_over_the_2008_2018_period_Results_from_the_Hellenic_Cardiology_Society_Ablation_Registry","translated_internal_url":"","created_at":"2023-11-02T16:08:48.901-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Trends_in_ablation_procedures_in_Greece_over_the_2008_2018_period_Results_from_the_Hellenic_Cardiology_Society_Ablation_Registry","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":467441,"name":"Ablation","url":"https://www.academia.edu/Documents/in/Ablation"}],"urls":[{"id":35168847,"url":"https://doi.org/10.1016/j.hjc.2020.09.005"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="108825189"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/108825189/Radiofrequency_ablation_procedures_in_Greece_initial_experience_and_results_from_the_national_registry_2008_2010"><img alt="Research paper thumbnail of Radiofrequency ablation procedures in Greece: initial experience and results from the national registry 2008-2010" class="work-thumbnail" src="https://attachments.academia-assets.com/107193311/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/108825189/Radiofrequency_ablation_procedures_in_Greece_initial_experience_and_results_from_the_national_registry_2008_2010">Radiofrequency ablation procedures in Greece: initial experience and results from the national registry 2008-2010</a></div><div class="wp-workCard_item"><span>Hellenic journal of cardiology : HJC = Hellēnikē kardiologikē epitheōrēsē</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In 2008 the radiofrequency ablation procedures (RFA) registry of the Hellenic Cardiological Socie...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In 2008 the radiofrequency ablation procedures (RFA) registry of the Hellenic Cardiological Society (HCS) was created. This is a dynamic, web-based application, which acts as the interface for storing and retrieving patients&#39; demographic data and ablation procedures. Access to the site is permitted only to registered users. The purpose of this study is to report the results of RFA procedures performed in Greece over the 2008-2010 period. There are 27 centers in 24 hospitals that are licensed to perform RFA in Greece. During the period 2008-2010, 3541 RFA procedures were performed in 3344 patients in 23 centers. Four centers did not contribute data at all for various reasons. It is interesting that nearly 50% of the total number of procedures were performed at 3 high volume centers (&gt;100 cases/year). The most common procedure was slow pathway ablation for atrioventricular reentrant tachycardia, the second was ablation of accessory pathway related tachycardias, and the third wa...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="89e38b5b995bda8eb896392579bcfd0f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107193311,"asset_id":108825189,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107193311/download_file?st=MTczMjc3OTEwNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="108825189"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="108825189"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 108825189; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=108825189]").text(description); $(".js-view-count[data-work-id=108825189]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 108825189; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='108825189']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 108825189, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "89e38b5b995bda8eb896392579bcfd0f" } } $('.js-work-strip[data-work-id=108825189]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":108825189,"title":"Radiofrequency ablation procedures in Greece: initial experience and results from the national registry 2008-2010","translated_title":"","metadata":{"abstract":"In 2008 the radiofrequency ablation procedures (RFA) registry of the Hellenic Cardiological Society (HCS) was created. This is a dynamic, web-based application, which acts as the interface for storing and retrieving patients\u0026#39; demographic data and ablation procedures. Access to the site is permitted only to registered users. The purpose of this study is to report the results of RFA procedures performed in Greece over the 2008-2010 period. There are 27 centers in 24 hospitals that are licensed to perform RFA in Greece. During the period 2008-2010, 3541 RFA procedures were performed in 3344 patients in 23 centers. Four centers did not contribute data at all for various reasons. It is interesting that nearly 50% of the total number of procedures were performed at 3 high volume centers (\u0026gt;100 cases/year). The most common procedure was slow pathway ablation for atrioventricular reentrant tachycardia, the second was ablation of accessory pathway related tachycardias, and the third wa...","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"Hellenic journal of cardiology : HJC = Hellēnikē kardiologikē epitheōrēsē"},"translated_abstract":"In 2008 the radiofrequency ablation procedures (RFA) registry of the Hellenic Cardiological Society (HCS) was created. This is a dynamic, web-based application, which acts as the interface for storing and retrieving patients\u0026#39; demographic data and ablation procedures. Access to the site is permitted only to registered users. The purpose of this study is to report the results of RFA procedures performed in Greece over the 2008-2010 period. There are 27 centers in 24 hospitals that are licensed to perform RFA in Greece. During the period 2008-2010, 3541 RFA procedures were performed in 3344 patients in 23 centers. Four centers did not contribute data at all for various reasons. It is interesting that nearly 50% of the total number of procedures were performed at 3 high volume centers (\u0026gt;100 cases/year). 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134984"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134984/Design_and_evaluation_of_mobile_scenario_based_learning_in_the_self_management_of_chronic_pain"><img alt="Research paper thumbnail of Design and evaluation of mobile scenario based learning in the self-management of chronic pain" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134984/Design_and_evaluation_of_mobile_scenario_based_learning_in_the_self_management_of_chronic_pain">Design and evaluation of mobile scenario based learning in the self-management of chronic pain</a></div><div class="wp-workCard_item"><span>Health Informatics Journal</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Chronic pain is a lifelong issue, being one of the main causes of disability, affecting a great n...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Chronic pain is a lifelong issue, being one of the main causes of disability, affecting a great number of people worldwide, many of which often avoid seeking medical advice from pain experts and/or demonstrate poor adherence to their therapeutic plan. One of the most important steps in achieving a manageable course of disease, is the ability of self-management. We aimed at applying a method of systematic patient education and self-management through the use of Virtual Patients (VPs), a well-established method for educating medical doctors and students but never before targeting patients. Two VPs scenarios were designed, tested and evaluated by patients with rheumatic disorders, achieving a SUS score of 88/100 “Best Imaginable”, alongside with positive reviews from the participants. The positive feedback from the patients supports the potential of VP educational paradigm to educate these patients and equip them with disease coping skills and strategies.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134984"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134984"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134984; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134984]").text(description); $(".js-view-count[data-work-id=103134984]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134984; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134984']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134984, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=103134984]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134984,"title":"Design and evaluation of mobile scenario based learning in the self-management of chronic pain","translated_title":"","metadata":{"abstract":"Chronic pain is a lifelong issue, being one of the main causes of disability, affecting a great number of people worldwide, many of which often avoid seeking medical advice from pain experts and/or demonstrate poor adherence to their therapeutic plan. One of the most important steps in achieving a manageable course of disease, is the ability of self-management. We aimed at applying a method of systematic patient education and self-management through the use of Virtual Patients (VPs), a well-established method for educating medical doctors and students but never before targeting patients. Two VPs scenarios were designed, tested and evaluated by patients with rheumatic disorders, achieving a SUS score of 88/100 “Best Imaginable”, alongside with positive reviews from the participants. 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Two VPs scenarios were designed, tested and evaluated by patients with rheumatic disorders, achieving a SUS score of 88/100 “Best Imaginable”, alongside with positive reviews from the participants. The positive feedback from the patients supports the potential of VP educational paradigm to educate these patients and equip them with disease coping skills and strategies.","internal_url":"https://www.academia.edu/103134984/Design_and_evaluation_of_mobile_scenario_based_learning_in_the_self_management_of_chronic_pain","translated_internal_url":"","created_at":"2023-06-10T00:03:28.111-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Design_and_evaluation_of_mobile_scenario_based_learning_in_the_self_management_of_chronic_pain","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3317,"name":"Health Informatics","url":"https://www.academia.edu/Documents/in/Health_Informatics"},{"id":16841,"name":"Self Management","url":"https://www.academia.edu/Documents/in/Self_Management"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":59587,"name":"Library and Information Studies","url":"https://www.academia.edu/Documents/in/Library_and_Information_Studies"}],"urls":[{"id":32151425,"url":"http://journals.sagepub.com/doi/pdf/10.1177/1460458220977575"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134981"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134981/Analytical_exploratory_tool_for_healthcare_professionals_to_monitor_cancer_patients_progress"><img alt="Research paper thumbnail of Analytical exploratory tool for healthcare professionals to monitor cancer patients’ progress" class="work-thumbnail" src="https://attachments.academia-assets.com/103222658/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134981/Analytical_exploratory_tool_for_healthcare_professionals_to_monitor_cancer_patients_progress">Analytical exploratory tool for healthcare professionals to monitor cancer patients’ progress</a></div><div class="wp-workCard_item"><span>Frontiers in Oncology</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">IntroductionCancer is a primary public concern in the European continent. Due to the large case n...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">IntroductionCancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, a large quantity of data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities.MethodsFollowing a co-creation, an interactive approach thanks to the Interactive Process Mining paradigm, and data from a tertiary hospital, we developed an exploratory tool to present cancer patients&#39; progress over time.ResultsThis work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical re...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="65f52de207fadea168ec12f73486d764" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103222658,"asset_id":103134981,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103222658/download_file?st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134981"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134981"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134981; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134981]").text(description); $(".js-view-count[data-work-id=103134981]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134981; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134981']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134981, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "65f52de207fadea168ec12f73486d764" } } $('.js-work-strip[data-work-id=103134981]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134981,"title":"Analytical exploratory tool for healthcare professionals to monitor cancer patients’ progress","translated_title":"","metadata":{"abstract":"IntroductionCancer is a primary public concern in the European continent. 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Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134980"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134980/The_Greek_translation_and_validation_of_the_electronic_version_of_the_Meno_D_rating_scale_for_post_menopausal_depression"><img alt="Research paper thumbnail of The Greek translation and validation of the electronic version of the Meno-D rating scale for post-menopausal depression" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134980/The_Greek_translation_and_validation_of_the_electronic_version_of_the_Meno_D_rating_scale_for_post_menopausal_depression">The Greek translation and validation of the electronic version of the Meno-D rating scale for post-menopausal depression</a></div><div class="wp-workCard_item"><span>Health Informatics Journal</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Menopausal transition and post-menopause constitute windows of increased vulnerability to depress...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Menopausal transition and post-menopause constitute windows of increased vulnerability to depression. Recently, the Meno-D was introduced, a novel 12-item, with five distinct subscales. The aim of our study was to translate and validate the electronic version of the Meno-D among Greek post-menopausal women. Translation and back-translation were performed by an expert group, while face validity was assessed by five experts. Along with the Beck Depression Inventory-II, the Meno-D scale was distributed online to 502 post-menopausal women. A confirmatory factor analysis was performed to investigate construct validity and both convergent and discriminant validity were evaluated. The data analysis was performed using Statistical Package for Social Sciences and AMOS. The 5-factor model of Meno-D achieved adequate levels of goodness-of-fit indices, scoring lower values in discriminant validity examined with heterotrait-monotrait ratio and composite reliability. The significant correlation w...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134980"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134980]").text(description); $(".js-view-count[data-work-id=103134980]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134980']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134980, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=103134980]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134980,"title":"The Greek translation and validation of the electronic version of the Meno-D rating scale for post-menopausal depression","translated_title":"","metadata":{"abstract":"Menopausal transition and post-menopause constitute windows of increased vulnerability to depression. Recently, the Meno-D was introduced, a novel 12-item, with five distinct subscales. The aim of our study was to translate and validate the electronic version of the Meno-D among Greek post-menopausal women. Translation and back-translation were performed by an expert group, while face validity was assessed by five experts. Along with the Beck Depression Inventory-II, the Meno-D scale was distributed online to 502 post-menopausal women. A confirmatory factor analysis was performed to investigate construct validity and both convergent and discriminant validity were evaluated. The data analysis was performed using Statistical Package for Social Sciences and AMOS. The 5-factor model of Meno-D achieved adequate levels of goodness-of-fit indices, scoring lower values in discriminant validity examined with heterotrait-monotrait ratio and composite reliability. The significant correlation w...","publisher":"SAGE Publications","publication_name":"Health Informatics Journal"},"translated_abstract":"Menopausal transition and post-menopause constitute windows of increased vulnerability to depression. Recently, the Meno-D was introduced, a novel 12-item, with five distinct subscales. The aim of our study was to translate and validate the electronic version of the Meno-D among Greek post-menopausal women. Translation and back-translation were performed by an expert group, while face validity was assessed by five experts. Along with the Beck Depression Inventory-II, the Meno-D scale was distributed online to 502 post-menopausal women. A confirmatory factor analysis was performed to investigate construct validity and both convergent and discriminant validity were evaluated. The data analysis was performed using Statistical Package for Social Sciences and AMOS. The 5-factor model of Meno-D achieved adequate levels of goodness-of-fit indices, scoring lower values in discriminant validity examined with heterotrait-monotrait ratio and composite reliability. The significant correlation w...","internal_url":"https://www.academia.edu/103134980/The_Greek_translation_and_validation_of_the_electronic_version_of_the_Meno_D_rating_scale_for_post_menopausal_depression","translated_internal_url":"","created_at":"2023-06-10T00:03:24.019-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Greek_translation_and_validation_of_the_electronic_version_of_the_Meno_D_rating_scale_for_post_menopausal_depression","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology"},{"id":226,"name":"Clinical Psychology","url":"https://www.academia.edu/Documents/in/Clinical_Psychology"},{"id":3317,"name":"Health Informatics","url":"https://www.academia.edu/Documents/in/Health_Informatics"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":59587,"name":"Library and Information Studies","url":"https://www.academia.edu/Documents/in/Library_and_Information_Studies"},{"id":104428,"name":"Confirmatory factor analysis","url":"https://www.academia.edu/Documents/in/Confirmatory_factor_analysis"},{"id":104434,"name":"Construct Validity","url":"https://www.academia.edu/Documents/in/Construct_Validity"},{"id":195969,"name":"Beck Depression Inventory","url":"https://www.academia.edu/Documents/in/Beck_Depression_Inventory"},{"id":245071,"name":"Rating Scale","url":"https://www.academia.edu/Documents/in/Rating_Scale"},{"id":349008,"name":"Convergent Validity","url":"https://www.academia.edu/Documents/in/Convergent_Validity"},{"id":843421,"name":"Discriminant Validity","url":"https://www.academia.edu/Documents/in/Discriminant_Validity"}],"urls":[{"id":32151422,"url":"http://journals.sagepub.com/doi/pdf/10.1177/14604582221080100"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134977"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134977/Multidataset_Incremental_Training_for_Optic_Disc_Segmentation"><img alt="Research paper thumbnail of Multidataset Incremental Training for Optic Disc Segmentation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134977/Multidataset_Incremental_Training_for_Optic_Disc_Segmentation">Multidataset Incremental Training for Optic Disc Segmentation</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">When convolutional neural networks are applied to image segmentation results depend greatly on th...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">When convolutional neural networks are applied to image segmentation results depend greatly on the data sets used to train the networks. Cloud providers support multi GPU and TPU virtual machines making the idea of cloud-based segmentation as service attractive. In this paper we study the problem of building a segmentation service, where images would come from different acquisition instruments, by training a generalized U-Net with images from a single or several datasets. We also study the possibility of training with a single instrument and perform quick retrains when more data is available. As our example we perform segmentation of Optic Disc in fundus images which is useful for glaucoma diagnosis. We use two publicly available data sets (RIM-One V3, DRISHTI) for individual, mixed or incremental training. We show that multidataset or incremental training can produce results that are similar to those published by researchers who use the same dataset for both training and validation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134977"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134977"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134977; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134977]").text(description); $(".js-view-count[data-work-id=103134977]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134977; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134977']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134977, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=103134977]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134977,"title":"Multidataset Incremental Training for Optic Disc Segmentation","translated_title":"","metadata":{"abstract":"When convolutional neural networks are applied to image segmentation results depend greatly on the data sets used to train the networks. 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We show that multidataset or incremental training can produce results that are similar to those published by researchers who use the same dataset for both training and validation.","publisher":"EANN","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"When convolutional neural networks are applied to image segmentation results depend greatly on the data sets used to train the networks. Cloud providers support multi GPU and TPU virtual machines making the idea of cloud-based segmentation as service attractive. In this paper we study the problem of building a segmentation service, where images would come from different acquisition instruments, by training a generalized U-Net with images from a single or several datasets. We also study the possibility of training with a single instrument and perform quick retrains when more data is available. 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We show that multidataset or incremental training can produce results that are similar to those published by researchers who use the same dataset for both training and validation.","internal_url":"https://www.academia.edu/103134977/Multidataset_Incremental_Training_for_Optic_Disc_Segmentation","translated_internal_url":"","created_at":"2023-06-10T00:03:22.540-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Multidataset_Incremental_Training_for_Optic_Disc_Segmentation","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":26860,"name":"Cloud Computing","url":"https://www.academia.edu/Documents/in/Cloud_Computing"},{"id":93217,"name":"Segmentation","url":"https://www.academia.edu/Documents/in/Segmentation"},{"id":1568111,"name":"Convolutional Neural Network","url":"https://www.academia.edu/Documents/in/Convolutional_Neural_Network"},{"id":3647879,"name":"Springer Ebooks","url":"https://www.academia.edu/Documents/in/Springer_Ebooks"}],"urls":[{"id":32151420,"url":"https://doi.org/10.1007/978-3-030-48791-1_28"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134970"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134970/A_Collective_Intelligence_Platform_to_Support_Older_Cancer_Survivors_Towards_the_Definition_of_LifeChamps_System_and_Big_Data_Reference_Architecture"><img alt="Research paper thumbnail of A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture" class="work-thumbnail" src="https://attachments.academia-assets.com/103222591/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134970/A_Collective_Intelligence_Platform_to_Support_Older_Cancer_Survivors_Towards_the_Definition_of_LifeChamps_System_and_Big_Data_Reference_Architecture">A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture</a></div><div class="wp-workCard_item"><span>MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Within the most recent years, most of the cancer patients are older age, which implies the necess...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the “4+1 architectural model”.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ea0e7c57f9c1272d5a66130bf6cffa05" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103222591,"asset_id":103134970,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103222591/download_file?st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134970"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134970"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134970; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134970]").text(description); $(".js-view-count[data-work-id=103134970]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134970; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134970']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134970, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ea0e7c57f9c1272d5a66130bf6cffa05" } } $('.js-work-strip[data-work-id=103134970]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134970,"title":"A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture","translated_title":"","metadata":{"abstract":"Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91903492"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91903492/AI_driven_prediction_for_the_disposition_of_medium_risk_incidents_visiting_emergency_departments"><img alt="Research paper thumbnail of AI-driven prediction for the disposition of medium-risk incidents visiting emergency departments" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91903492/AI_driven_prediction_for_the_disposition_of_medium_risk_incidents_visiting_emergency_departments">AI-driven prediction for the disposition of medium-risk incidents visiting emergency departments</a></div><div class="wp-workCard_item"><span>25th Pan-Hellenic Conference on Informatics</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Emergency Departments globally suffer overcrowding due to the lack of adequate capacity and/or gu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Emergency Departments globally suffer overcrowding due to the lack of adequate capacity and/or guidelines and policies for triaging patients. Medical service quality improvement requires optimal patient prioritization according to their level of urgency. This study aims to evaluate classification models predicting the admission or discharge of incidents triaged as level 3 according to the Emergency Severity Index algorithm. As such, adult patient visits were examined from a publicly available dataset. Feature Importance was used for the assessment of each variable contribution in predictions and a subset of 196 out of 972 variables of the original dataset was sampled. XGBoost, random forest, convolutional neural network, and k-nearest neighbors algorithms were deployed and evaluated regarding hospitalization prediction. Convolutional neural network utilization required a tabular data to image transformation which was applied using the Image Data Generator to Tabular Data (IGTD) algorithm. Benchmarking among the four algorithms showed that XGBoost outperformed the others, achieving an accuracy of 0.75, an area under the receiver operating curve of 0.74 and an area under the precision-recall curve of 0.54. Overall, machine learning-based assessment of the disposition of medium-risk patients according to ESI may lead to predictive models which constitute useful decision support tools.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91903492"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91903492"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91903492; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91903492]").text(description); $(".js-view-count[data-work-id=91903492]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91903492; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91903492']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91903492, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91903492]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91903492,"title":"AI-driven prediction for the disposition of medium-risk incidents visiting emergency departments","translated_title":"","metadata":{"abstract":"Emergency Departments globally suffer overcrowding due to the lack of adequate capacity and/or guidelines and policies for triaging patients. Medical service quality improvement requires optimal patient prioritization according to their level of urgency. This study aims to evaluate classification models predicting the admission or discharge of incidents triaged as level 3 according to the Emergency Severity Index algorithm. As such, adult patient visits were examined from a publicly available dataset. Feature Importance was used for the assessment of each variable contribution in predictions and a subset of 196 out of 972 variables of the original dataset was sampled. XGBoost, random forest, convolutional neural network, and k-nearest neighbors algorithms were deployed and evaluated regarding hospitalization prediction. Convolutional neural network utilization required a tabular data to image transformation which was applied using the Image Data Generator to Tabular Data (IGTD) algorithm. Benchmarking among the four algorithms showed that XGBoost outperformed the others, achieving an accuracy of 0.75, an area under the receiver operating curve of 0.74 and an area under the precision-recall curve of 0.54. Overall, machine learning-based assessment of the disposition of medium-risk patients according to ESI may lead to predictive models which constitute useful decision support tools.","publisher":"ACM","publication_name":"25th Pan-Hellenic Conference on Informatics"},"translated_abstract":"Emergency Departments globally suffer overcrowding due to the lack of adequate capacity and/or guidelines and policies for triaging patients. Medical service quality improvement requires optimal patient prioritization according to their level of urgency. This study aims to evaluate classification models predicting the admission or discharge of incidents triaged as level 3 according to the Emergency Severity Index algorithm. As such, adult patient visits were examined from a publicly available dataset. Feature Importance was used for the assessment of each variable contribution in predictions and a subset of 196 out of 972 variables of the original dataset was sampled. XGBoost, random forest, convolutional neural network, and k-nearest neighbors algorithms were deployed and evaluated regarding hospitalization prediction. Convolutional neural network utilization required a tabular data to image transformation which was applied using the Image Data Generator to Tabular Data (IGTD) algorithm. Benchmarking among the four algorithms showed that XGBoost outperformed the others, achieving an accuracy of 0.75, an area under the receiver operating curve of 0.74 and an area under the precision-recall curve of 0.54. Overall, machine learning-based assessment of the disposition of medium-risk patients according to ESI may lead to predictive models which constitute useful decision support tools.","internal_url":"https://www.academia.edu/91903492/AI_driven_prediction_for_the_disposition_of_medium_risk_incidents_visiting_emergency_departments","translated_internal_url":"","created_at":"2022-11-29T23:58:41.211-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"AI_driven_prediction_for_the_disposition_of_medium_risk_incidents_visiting_emergency_departments","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":92258,"name":"Disposition","url":"https://www.academia.edu/Documents/in/Disposition"}],"urls":[{"id":26538184,"url":"https://dl.acm.org/doi/pdf/10.1145/3503823.3503912"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91903490"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91903490/A_Patient_Oriented_App_ThessHF_to_Improve_Self_Care_Quality_in_Heart_Failure_From_Evidence_Based_Design_to_Pilot_Study"><img alt="Research paper thumbnail of A Patient-Oriented App (ThessHF) to Improve Self-Care Quality in Heart Failure: From Evidence-Based Design to Pilot Study" class="work-thumbnail" src="https://attachments.academia-assets.com/95058934/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91903490/A_Patient_Oriented_App_ThessHF_to_Improve_Self_Care_Quality_in_Heart_Failure_From_Evidence_Based_Design_to_Pilot_Study">A Patient-Oriented App (ThessHF) to Improve Self-Care Quality in Heart Failure: From Evidence-Based Design to Pilot Study</a></div><div class="wp-workCard_item"><span>JMIR mHealth and uHealth</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background Heart failure (HF) remains a major public health challenge, while HF self-care is part...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background Heart failure (HF) remains a major public health challenge, while HF self-care is particularly challenging. Mobile health (mHealth)–based interventions taking advantage of smartphone technology have shown particular promise in increasing the quality of self-care among these patients, and in turn improving the outcomes of their disease. Objective The objective of this study was to co-develop with physicians, patients with HF, and their caregivers a patient-oriented mHealth app, perform usability assessment, and investigate its effect on the quality of life of patients with HF and rate of hospitalizations in a pilot study. Methods The development of an mHealth app (The Hellenic Educational Self-care and Support Heart Failure app [ThessHF app]) was evidence based, including features based on previous clinically tested mHealth interventions and selected by a panel of HF expert physicians and discussed with patients with HF. At the end of alpha development, the app was rated b...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b24f3103ece619496a1fb88798159074" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95058934,"asset_id":91903490,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95058934/download_file?st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91903490"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91903490"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91903490; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91903490]").text(description); $(".js-view-count[data-work-id=91903490]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91903490; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91903490']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91903490, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b24f3103ece619496a1fb88798159074" } } $('.js-work-strip[data-work-id=91903490]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91903490,"title":"A Patient-Oriented App (ThessHF) to Improve Self-Care Quality in Heart Failure: From Evidence-Based Design to Pilot Study","translated_title":"","metadata":{"abstract":"Background Heart failure (HF) remains a major public health challenge, while HF self-care is particularly challenging. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="115058480"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115058480/Creative_Art_Therapy_as_an_Efficient_Way_to_Improve_the_Well_Being_of_People_Living_with_Dementia"><img alt="Research paper thumbnail of Creative Art Therapy as an Efficient Way to Improve the Well-Being of People Living with Dementia" class="work-thumbnail" src="https://attachments.academia-assets.com/111577555/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115058480/Creative_Art_Therapy_as_an_Efficient_Way_to_Improve_the_Well_Being_of_People_Living_with_Dementia">Creative Art Therapy as an Efficient Way to Improve the Well-Being of People Living with Dementia</a></div><div class="wp-workCard_item"><span>Studies in health technology and informatics</span><span>, Jun 29, 2023</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b33a6cb4bcebf01214729aa7e69d8f86" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111577555,"asset_id":115058480,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111577555/download_file?st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115058480"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115058480"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115058480; 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src="https://attachments.academia-assets.com/111577558/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115058479/Scalable_real_time_health_data_sensing_and_analysis_enabling_collaborative_care_delivery">Scalable real‑time health data sensing and analysis enabling collaborative care delivery</a></div><div class="wp-workCard_item"><span>Zenodo (CERN European Organization for Nuclear Research)</span><span>, Jun 20, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="01358e10f925ec9c9fe121a9c5553847" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111577558,"asset_id":115058479,"asset_type":"Work","button_location":"profile"}" 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="115058477"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115058477/Older_adults_healthcare_needs_and_expectations_of_a_developing_digital_platform_to_enhance_supportive_care_post_treatment_Co_creation_across_four_countries"><img alt="Research paper thumbnail of Older adults’ healthcare needs and expectations of a developing digital platform to enhance supportive care post treatment: Co-creation across four countries" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115058477/Older_adults_healthcare_needs_and_expectations_of_a_developing_digital_platform_to_enhance_supportive_care_post_treatment_Co_creation_across_four_countries">Older adults’ healthcare needs and expectations of a developing digital platform to enhance supportive care post treatment: Co-creation across four countries</a></div><div class="wp-workCard_item"><span>Journal of Geriatric Oncology</span><span>, Dec 1, 2021</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115058477"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115058477"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115058477; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="115058476"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115058476/A_multinational_investigation_of_healthcare_needs_preferences_and_expectations_in_supportive_cancer_care_co_creating_the_LifeChamps_digital_platform"><img alt="Research paper thumbnail of A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform" class="work-thumbnail" src="https://attachments.academia-assets.com/111577554/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115058476/A_multinational_investigation_of_healthcare_needs_preferences_and_expectations_in_supportive_cancer_care_co_creating_the_LifeChamps_digital_platform">A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform</a></div><div class="wp-workCard_item"><span>Journal of Cancer Survivorship</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Purpose This study is to evaluate healthcare needs, preferences, and expectations in supportive c...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Purpose This study is to evaluate healthcare needs, preferences, and expectations in supportive cancer care as perceived by cancer survivors, family caregivers, and healthcare professionals. Methods Key stakeholders consisted of cancer survivors diagnosed with breast cancer, prostate cancer, or melanoma; adult family caregivers; and healthcare professionals involved in oncology. Recruitment was via several routes, and data were collected via either online surveys or telephone interviews in Greece, Spain, Sweden, and the UK. Framework analysis was applied to the dataset. Results One hundred and fifty-five stakeholders participated: 70 cancer survivors, 23 family caregivers, and 62 healthcare professionals (13 clinical roles). Cancer survivors and family caregivers’ needs included information and support on practical/daily living, as frustration was apparent with the lack of follow-up services. Healthcare professionals agreed on a multidisciplinary health service with a “focus on the ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5cb578d058f534e4d55b6063459f19cb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":111577554,"asset_id":115058476,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/111577554/download_file?st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&st=MTczMjc3OTEwNyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115058476"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115058476"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115058476; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=115058476]").text(description); $(".js-view-count[data-work-id=115058476]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 115058476; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='115058476']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 115058476, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5cb578d058f534e4d55b6063459f19cb" } } $('.js-work-strip[data-work-id=115058476]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":115058476,"title":"A multinational investigation of healthcare needs, preferences, and expectations in supportive cancer care: co-creating the LifeChamps digital platform","translated_title":"","metadata":{"abstract":"Purpose This study is to evaluate healthcare needs, preferences, and expectations in supportive cancer care as perceived by cancer survivors, family caregivers, and healthcare professionals. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="108825195"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/108825195/Trends_in_ablation_procedures_in_Greece_over_the_2008_2018_period_Results_from_the_Hellenic_Cardiology_Society_Ablation_Registry"><img alt="Research paper thumbnail of Trends in ablation procedures in Greece over the 2008-2018 period: Results from the Hellenic Cardiology Society Ablation Registry" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/108825195/Trends_in_ablation_procedures_in_Greece_over_the_2008_2018_period_Results_from_the_Hellenic_Cardiology_Society_Ablation_Registry">Trends in ablation procedures in Greece over the 2008-2018 period: Results from the Hellenic Cardiology Society Ablation Registry</a></div><div class="wp-workCard_item"><span>Hellenic Journal of Cardiology</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">BACKGROUND In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">BACKGROUND In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society of Cardiology was created. This online database allowed electrophysiologists around the country to input data for all performed ablation procedures. The aim of this study is to provide a thorough report and interpretation of the data submitted to the registry between 2008 and 2018. METHODS In 2008, a total of 27 centers/medical teams in 24 hospitals were licensed to perform RFA in Greece. By 2018, the number had risen to 31. Each center was tasked with inserting their own data into the registry, which included patient demographics (anonymized), type of procedure and technique, complications, and outcomes. RESULTS A total of 18587 procedures in 17900 patients were recorded in the period of 2008-2018. By 2018, slightly more than 70% of procedures were performed in 7 high-volume centers (&gt;100 cases/year). The most common procedure since 2014 was atrial fibrillation ablation, followed by atrioventricular nodal reentry tachycardia ablation. Complication rates were low, and success rates remained high, whereas the 6-month relapse rates declined steadily. CONCLUSION This online RFA registry has proved that ablation procedures in Greece have reached a very high standard, with results and complication rates comparable to European and American standards. Ablation procedures for atrial fibrillation are increasing constantly, with it being the most common intervention over the last 6-year period, although the absolute number of procedures still remains low, compared to other European countries.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="108825195"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="108825195"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 108825195; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=108825195]").text(description); $(".js-view-count[data-work-id=108825195]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 108825195; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='108825195']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 108825195, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=108825195]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":108825195,"title":"Trends in ablation procedures in Greece over the 2008-2018 period: Results from the Hellenic Cardiology Society Ablation Registry","translated_title":"","metadata":{"abstract":"BACKGROUND In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society of Cardiology was created. This online database allowed electrophysiologists around the country to input data for all performed ablation procedures. The aim of this study is to provide a thorough report and interpretation of the data submitted to the registry between 2008 and 2018. METHODS In 2008, a total of 27 centers/medical teams in 24 hospitals were licensed to perform RFA in Greece. By 2018, the number had risen to 31. Each center was tasked with inserting their own data into the registry, which included patient demographics (anonymized), type of procedure and technique, complications, and outcomes. RESULTS A total of 18587 procedures in 17900 patients were recorded in the period of 2008-2018. By 2018, slightly more than 70% of procedures were performed in 7 high-volume centers (\u0026gt;100 cases/year). The most common procedure since 2014 was atrial fibrillation ablation, followed by atrioventricular nodal reentry tachycardia ablation. Complication rates were low, and success rates remained high, whereas the 6-month relapse rates declined steadily. CONCLUSION This online RFA registry has proved that ablation procedures in Greece have reached a very high standard, with results and complication rates comparable to European and American standards. Ablation procedures for atrial fibrillation are increasing constantly, with it being the most common intervention over the last 6-year period, although the absolute number of procedures still remains low, compared to other European countries.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2021,"errors":{}},"publication_name":"Hellenic Journal of Cardiology"},"translated_abstract":"BACKGROUND In 2008, the radiofrequency ablation (RFA) procedures registry of the Hellenic Society of Cardiology was created. This online database allowed electrophysiologists around the country to input data for all performed ablation procedures. The aim of this study is to provide a thorough report and interpretation of the data submitted to the registry between 2008 and 2018. METHODS In 2008, a total of 27 centers/medical teams in 24 hospitals were licensed to perform RFA in Greece. By 2018, the number had risen to 31. Each center was tasked with inserting their own data into the registry, which included patient demographics (anonymized), type of procedure and technique, complications, and outcomes. RESULTS A total of 18587 procedures in 17900 patients were recorded in the period of 2008-2018. By 2018, slightly more than 70% of procedures were performed in 7 high-volume centers (\u0026gt;100 cases/year). The most common procedure since 2014 was atrial fibrillation ablation, followed by atrioventricular nodal reentry tachycardia ablation. Complication rates were low, and success rates remained high, whereas the 6-month relapse rates declined steadily. CONCLUSION This online RFA registry has proved that ablation procedures in Greece have reached a very high standard, with results and complication rates comparable to European and American standards. Ablation procedures for atrial fibrillation are increasing constantly, with it being the most common intervention over the last 6-year period, although the absolute number of procedures still remains low, compared to other European countries.","internal_url":"https://www.academia.edu/108825195/Trends_in_ablation_procedures_in_Greece_over_the_2008_2018_period_Results_from_the_Hellenic_Cardiology_Society_Ablation_Registry","translated_internal_url":"","created_at":"2023-11-02T16:08:48.901-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Trends_in_ablation_procedures_in_Greece_over_the_2008_2018_period_Results_from_the_Hellenic_Cardiology_Society_Ablation_Registry","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":467441,"name":"Ablation","url":"https://www.academia.edu/Documents/in/Ablation"}],"urls":[{"id":35168847,"url":"https://doi.org/10.1016/j.hjc.2020.09.005"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="108825189"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/108825189/Radiofrequency_ablation_procedures_in_Greece_initial_experience_and_results_from_the_national_registry_2008_2010"><img alt="Research paper thumbnail of Radiofrequency ablation procedures in Greece: initial experience and results from the national registry 2008-2010" class="work-thumbnail" src="https://attachments.academia-assets.com/107193311/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/108825189/Radiofrequency_ablation_procedures_in_Greece_initial_experience_and_results_from_the_national_registry_2008_2010">Radiofrequency ablation procedures in Greece: initial experience and results from the national registry 2008-2010</a></div><div class="wp-workCard_item"><span>Hellenic journal of cardiology : HJC = Hellēnikē kardiologikē epitheōrēsē</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In 2008 the radiofrequency ablation procedures (RFA) registry of the Hellenic Cardiological Socie...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In 2008 the radiofrequency ablation procedures (RFA) registry of the Hellenic Cardiological Society (HCS) was created. This is a dynamic, web-based application, which acts as the interface for storing and retrieving patients&#39; demographic data and ablation procedures. Access to the site is permitted only to registered users. The purpose of this study is to report the results of RFA procedures performed in Greece over the 2008-2010 period. There are 27 centers in 24 hospitals that are licensed to perform RFA in Greece. During the period 2008-2010, 3541 RFA procedures were performed in 3344 patients in 23 centers. Four centers did not contribute data at all for various reasons. It is interesting that nearly 50% of the total number of procedures were performed at 3 high volume centers (&gt;100 cases/year). The most common procedure was slow pathway ablation for atrioventricular reentrant tachycardia, the second was ablation of accessory pathway related tachycardias, and the third wa...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="89e38b5b995bda8eb896392579bcfd0f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107193311,"asset_id":108825189,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107193311/download_file?st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="108825189"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="108825189"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 108825189; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=108825189]").text(description); $(".js-view-count[data-work-id=108825189]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 108825189; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='108825189']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 108825189, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "89e38b5b995bda8eb896392579bcfd0f" } } $('.js-work-strip[data-work-id=108825189]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":108825189,"title":"Radiofrequency ablation procedures in Greece: initial experience and results from the national registry 2008-2010","translated_title":"","metadata":{"abstract":"In 2008 the radiofrequency ablation procedures (RFA) registry of the Hellenic Cardiological Society (HCS) was created. This is a dynamic, web-based application, which acts as the interface for storing and retrieving patients\u0026#39; demographic data and ablation procedures. Access to the site is permitted only to registered users. The purpose of this study is to report the results of RFA procedures performed in Greece over the 2008-2010 period. There are 27 centers in 24 hospitals that are licensed to perform RFA in Greece. During the period 2008-2010, 3541 RFA procedures were performed in 3344 patients in 23 centers. Four centers did not contribute data at all for various reasons. It is interesting that nearly 50% of the total number of procedures were performed at 3 high volume centers (\u0026gt;100 cases/year). The most common procedure was slow pathway ablation for atrioventricular reentrant tachycardia, the second was ablation of accessory pathway related tachycardias, and the third wa...","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"Hellenic journal of cardiology : HJC = Hellēnikē kardiologikē epitheōrēsē"},"translated_abstract":"In 2008 the radiofrequency ablation procedures (RFA) registry of the Hellenic Cardiological Society (HCS) was created. This is a dynamic, web-based application, which acts as the interface for storing and retrieving patients\u0026#39; demographic data and ablation procedures. Access to the site is permitted only to registered users. The purpose of this study is to report the results of RFA procedures performed in Greece over the 2008-2010 period. There are 27 centers in 24 hospitals that are licensed to perform RFA in Greece. During the period 2008-2010, 3541 RFA procedures were performed in 3344 patients in 23 centers. Four centers did not contribute data at all for various reasons. It is interesting that nearly 50% of the total number of procedures were performed at 3 high volume centers (\u0026gt;100 cases/year). 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134984"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134984/Design_and_evaluation_of_mobile_scenario_based_learning_in_the_self_management_of_chronic_pain"><img alt="Research paper thumbnail of Design and evaluation of mobile scenario based learning in the self-management of chronic pain" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134984/Design_and_evaluation_of_mobile_scenario_based_learning_in_the_self_management_of_chronic_pain">Design and evaluation of mobile scenario based learning in the self-management of chronic pain</a></div><div class="wp-workCard_item"><span>Health Informatics Journal</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Chronic pain is a lifelong issue, being one of the main causes of disability, affecting a great n...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Chronic pain is a lifelong issue, being one of the main causes of disability, affecting a great number of people worldwide, many of which often avoid seeking medical advice from pain experts and/or demonstrate poor adherence to their therapeutic plan. One of the most important steps in achieving a manageable course of disease, is the ability of self-management. We aimed at applying a method of systematic patient education and self-management through the use of Virtual Patients (VPs), a well-established method for educating medical doctors and students but never before targeting patients. Two VPs scenarios were designed, tested and evaluated by patients with rheumatic disorders, achieving a SUS score of 88/100 “Best Imaginable”, alongside with positive reviews from the participants. The positive feedback from the patients supports the potential of VP educational paradigm to educate these patients and equip them with disease coping skills and strategies.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134984"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134984"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134984; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134984]").text(description); $(".js-view-count[data-work-id=103134984]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134984; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134984']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134984, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=103134984]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134984,"title":"Design and evaluation of mobile scenario based learning in the self-management of chronic pain","translated_title":"","metadata":{"abstract":"Chronic pain is a lifelong issue, being one of the main causes of disability, affecting a great number of people worldwide, many of which often avoid seeking medical advice from pain experts and/or demonstrate poor adherence to their therapeutic plan. One of the most important steps in achieving a manageable course of disease, is the ability of self-management. We aimed at applying a method of systematic patient education and self-management through the use of Virtual Patients (VPs), a well-established method for educating medical doctors and students but never before targeting patients. Two VPs scenarios were designed, tested and evaluated by patients with rheumatic disorders, achieving a SUS score of 88/100 “Best Imaginable”, alongside with positive reviews from the participants. 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Two VPs scenarios were designed, tested and evaluated by patients with rheumatic disorders, achieving a SUS score of 88/100 “Best Imaginable”, alongside with positive reviews from the participants. The positive feedback from the patients supports the potential of VP educational paradigm to educate these patients and equip them with disease coping skills and strategies.","internal_url":"https://www.academia.edu/103134984/Design_and_evaluation_of_mobile_scenario_based_learning_in_the_self_management_of_chronic_pain","translated_internal_url":"","created_at":"2023-06-10T00:03:28.111-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Design_and_evaluation_of_mobile_scenario_based_learning_in_the_self_management_of_chronic_pain","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3317,"name":"Health Informatics","url":"https://www.academia.edu/Documents/in/Health_Informatics"},{"id":16841,"name":"Self Management","url":"https://www.academia.edu/Documents/in/Self_Management"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":59587,"name":"Library and Information Studies","url":"https://www.academia.edu/Documents/in/Library_and_Information_Studies"}],"urls":[{"id":32151425,"url":"http://journals.sagepub.com/doi/pdf/10.1177/1460458220977575"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134981"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134981/Analytical_exploratory_tool_for_healthcare_professionals_to_monitor_cancer_patients_progress"><img alt="Research paper thumbnail of Analytical exploratory tool for healthcare professionals to monitor cancer patients’ progress" class="work-thumbnail" src="https://attachments.academia-assets.com/103222658/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134981/Analytical_exploratory_tool_for_healthcare_professionals_to_monitor_cancer_patients_progress">Analytical exploratory tool for healthcare professionals to monitor cancer patients’ progress</a></div><div class="wp-workCard_item"><span>Frontiers in Oncology</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">IntroductionCancer is a primary public concern in the European continent. Due to the large case n...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">IntroductionCancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, a large quantity of data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities.MethodsFollowing a co-creation, an interactive approach thanks to the Interactive Process Mining paradigm, and data from a tertiary hospital, we developed an exploratory tool to present cancer patients&#39; progress over time.ResultsThis work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical re...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="65f52de207fadea168ec12f73486d764" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103222658,"asset_id":103134981,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103222658/download_file?st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134981"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134981"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134981; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134981]").text(description); $(".js-view-count[data-work-id=103134981]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134981; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134981']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134981, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "65f52de207fadea168ec12f73486d764" } } $('.js-work-strip[data-work-id=103134981]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134981,"title":"Analytical exploratory tool for healthcare professionals to monitor cancer patients’ progress","translated_title":"","metadata":{"abstract":"IntroductionCancer is a primary public concern in the European continent. 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Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134980"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134980/The_Greek_translation_and_validation_of_the_electronic_version_of_the_Meno_D_rating_scale_for_post_menopausal_depression"><img alt="Research paper thumbnail of The Greek translation and validation of the electronic version of the Meno-D rating scale for post-menopausal depression" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134980/The_Greek_translation_and_validation_of_the_electronic_version_of_the_Meno_D_rating_scale_for_post_menopausal_depression">The Greek translation and validation of the electronic version of the Meno-D rating scale for post-menopausal depression</a></div><div class="wp-workCard_item"><span>Health Informatics Journal</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Menopausal transition and post-menopause constitute windows of increased vulnerability to depress...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Menopausal transition and post-menopause constitute windows of increased vulnerability to depression. Recently, the Meno-D was introduced, a novel 12-item, with five distinct subscales. The aim of our study was to translate and validate the electronic version of the Meno-D among Greek post-menopausal women. Translation and back-translation were performed by an expert group, while face validity was assessed by five experts. Along with the Beck Depression Inventory-II, the Meno-D scale was distributed online to 502 post-menopausal women. A confirmatory factor analysis was performed to investigate construct validity and both convergent and discriminant validity were evaluated. The data analysis was performed using Statistical Package for Social Sciences and AMOS. The 5-factor model of Meno-D achieved adequate levels of goodness-of-fit indices, scoring lower values in discriminant validity examined with heterotrait-monotrait ratio and composite reliability. The significant correlation w...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134980"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134980]").text(description); $(".js-view-count[data-work-id=103134980]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134980']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134980, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=103134980]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134980,"title":"The Greek translation and validation of the electronic version of the Meno-D rating scale for post-menopausal depression","translated_title":"","metadata":{"abstract":"Menopausal transition and post-menopause constitute windows of increased vulnerability to depression. Recently, the Meno-D was introduced, a novel 12-item, with five distinct subscales. The aim of our study was to translate and validate the electronic version of the Meno-D among Greek post-menopausal women. Translation and back-translation were performed by an expert group, while face validity was assessed by five experts. Along with the Beck Depression Inventory-II, the Meno-D scale was distributed online to 502 post-menopausal women. A confirmatory factor analysis was performed to investigate construct validity and both convergent and discriminant validity were evaluated. The data analysis was performed using Statistical Package for Social Sciences and AMOS. The 5-factor model of Meno-D achieved adequate levels of goodness-of-fit indices, scoring lower values in discriminant validity examined with heterotrait-monotrait ratio and composite reliability. The significant correlation w...","publisher":"SAGE Publications","publication_name":"Health Informatics Journal"},"translated_abstract":"Menopausal transition and post-menopause constitute windows of increased vulnerability to depression. Recently, the Meno-D was introduced, a novel 12-item, with five distinct subscales. The aim of our study was to translate and validate the electronic version of the Meno-D among Greek post-menopausal women. Translation and back-translation were performed by an expert group, while face validity was assessed by five experts. Along with the Beck Depression Inventory-II, the Meno-D scale was distributed online to 502 post-menopausal women. A confirmatory factor analysis was performed to investigate construct validity and both convergent and discriminant validity were evaluated. The data analysis was performed using Statistical Package for Social Sciences and AMOS. The 5-factor model of Meno-D achieved adequate levels of goodness-of-fit indices, scoring lower values in discriminant validity examined with heterotrait-monotrait ratio and composite reliability. The significant correlation w...","internal_url":"https://www.academia.edu/103134980/The_Greek_translation_and_validation_of_the_electronic_version_of_the_Meno_D_rating_scale_for_post_menopausal_depression","translated_internal_url":"","created_at":"2023-06-10T00:03:24.019-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Greek_translation_and_validation_of_the_electronic_version_of_the_Meno_D_rating_scale_for_post_menopausal_depression","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems"},{"id":221,"name":"Psychology","url":"https://www.academia.edu/Documents/in/Psychology"},{"id":226,"name":"Clinical Psychology","url":"https://www.academia.edu/Documents/in/Clinical_Psychology"},{"id":3317,"name":"Health Informatics","url":"https://www.academia.edu/Documents/in/Health_Informatics"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"},{"id":59587,"name":"Library and Information Studies","url":"https://www.academia.edu/Documents/in/Library_and_Information_Studies"},{"id":104428,"name":"Confirmatory factor analysis","url":"https://www.academia.edu/Documents/in/Confirmatory_factor_analysis"},{"id":104434,"name":"Construct Validity","url":"https://www.academia.edu/Documents/in/Construct_Validity"},{"id":195969,"name":"Beck Depression Inventory","url":"https://www.academia.edu/Documents/in/Beck_Depression_Inventory"},{"id":245071,"name":"Rating Scale","url":"https://www.academia.edu/Documents/in/Rating_Scale"},{"id":349008,"name":"Convergent Validity","url":"https://www.academia.edu/Documents/in/Convergent_Validity"},{"id":843421,"name":"Discriminant Validity","url":"https://www.academia.edu/Documents/in/Discriminant_Validity"}],"urls":[{"id":32151422,"url":"http://journals.sagepub.com/doi/pdf/10.1177/14604582221080100"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134977"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134977/Multidataset_Incremental_Training_for_Optic_Disc_Segmentation"><img alt="Research paper thumbnail of Multidataset Incremental Training for Optic Disc Segmentation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134977/Multidataset_Incremental_Training_for_Optic_Disc_Segmentation">Multidataset Incremental Training for Optic Disc Segmentation</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">When convolutional neural networks are applied to image segmentation results depend greatly on th...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">When convolutional neural networks are applied to image segmentation results depend greatly on the data sets used to train the networks. Cloud providers support multi GPU and TPU virtual machines making the idea of cloud-based segmentation as service attractive. In this paper we study the problem of building a segmentation service, where images would come from different acquisition instruments, by training a generalized U-Net with images from a single or several datasets. We also study the possibility of training with a single instrument and perform quick retrains when more data is available. As our example we perform segmentation of Optic Disc in fundus images which is useful for glaucoma diagnosis. We use two publicly available data sets (RIM-One V3, DRISHTI) for individual, mixed or incremental training. We show that multidataset or incremental training can produce results that are similar to those published by researchers who use the same dataset for both training and validation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134977"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134977"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134977; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134977]").text(description); $(".js-view-count[data-work-id=103134977]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134977; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134977']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134977, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=103134977]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134977,"title":"Multidataset Incremental Training for Optic Disc Segmentation","translated_title":"","metadata":{"abstract":"When convolutional neural networks are applied to image segmentation results depend greatly on the data sets used to train the networks. 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We show that multidataset or incremental training can produce results that are similar to those published by researchers who use the same dataset for both training and validation.","publisher":"EANN","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"When convolutional neural networks are applied to image segmentation results depend greatly on the data sets used to train the networks. Cloud providers support multi GPU and TPU virtual machines making the idea of cloud-based segmentation as service attractive. In this paper we study the problem of building a segmentation service, where images would come from different acquisition instruments, by training a generalized U-Net with images from a single or several datasets. We also study the possibility of training with a single instrument and perform quick retrains when more data is available. 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We show that multidataset or incremental training can produce results that are similar to those published by researchers who use the same dataset for both training and validation.","internal_url":"https://www.academia.edu/103134977/Multidataset_Incremental_Training_for_Optic_Disc_Segmentation","translated_internal_url":"","created_at":"2023-06-10T00:03:22.540-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Multidataset_Incremental_Training_for_Optic_Disc_Segmentation","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":26860,"name":"Cloud Computing","url":"https://www.academia.edu/Documents/in/Cloud_Computing"},{"id":93217,"name":"Segmentation","url":"https://www.academia.edu/Documents/in/Segmentation"},{"id":1568111,"name":"Convolutional Neural Network","url":"https://www.academia.edu/Documents/in/Convolutional_Neural_Network"},{"id":3647879,"name":"Springer Ebooks","url":"https://www.academia.edu/Documents/in/Springer_Ebooks"}],"urls":[{"id":32151420,"url":"https://doi.org/10.1007/978-3-030-48791-1_28"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="103134970"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/103134970/A_Collective_Intelligence_Platform_to_Support_Older_Cancer_Survivors_Towards_the_Definition_of_LifeChamps_System_and_Big_Data_Reference_Architecture"><img alt="Research paper thumbnail of A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture" class="work-thumbnail" src="https://attachments.academia-assets.com/103222591/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/103134970/A_Collective_Intelligence_Platform_to_Support_Older_Cancer_Survivors_Towards_the_Definition_of_LifeChamps_System_and_Big_Data_Reference_Architecture">A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture</a></div><div class="wp-workCard_item"><span>MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Within the most recent years, most of the cancer patients are older age, which implies the necess...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the “4+1 architectural model”.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ea0e7c57f9c1272d5a66130bf6cffa05" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":103222591,"asset_id":103134970,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/103222591/download_file?st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103134970"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="103134970"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103134970; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103134970]").text(description); $(".js-view-count[data-work-id=103134970]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103134970; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103134970']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 103134970, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ea0e7c57f9c1272d5a66130bf6cffa05" } } $('.js-work-strip[data-work-id=103134970]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103134970,"title":"A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture","translated_title":"","metadata":{"abstract":"Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91903492"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91903492/AI_driven_prediction_for_the_disposition_of_medium_risk_incidents_visiting_emergency_departments"><img alt="Research paper thumbnail of AI-driven prediction for the disposition of medium-risk incidents visiting emergency departments" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91903492/AI_driven_prediction_for_the_disposition_of_medium_risk_incidents_visiting_emergency_departments">AI-driven prediction for the disposition of medium-risk incidents visiting emergency departments</a></div><div class="wp-workCard_item"><span>25th Pan-Hellenic Conference on Informatics</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Emergency Departments globally suffer overcrowding due to the lack of adequate capacity and/or gu...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Emergency Departments globally suffer overcrowding due to the lack of adequate capacity and/or guidelines and policies for triaging patients. Medical service quality improvement requires optimal patient prioritization according to their level of urgency. This study aims to evaluate classification models predicting the admission or discharge of incidents triaged as level 3 according to the Emergency Severity Index algorithm. As such, adult patient visits were examined from a publicly available dataset. Feature Importance was used for the assessment of each variable contribution in predictions and a subset of 196 out of 972 variables of the original dataset was sampled. XGBoost, random forest, convolutional neural network, and k-nearest neighbors algorithms were deployed and evaluated regarding hospitalization prediction. Convolutional neural network utilization required a tabular data to image transformation which was applied using the Image Data Generator to Tabular Data (IGTD) algorithm. Benchmarking among the four algorithms showed that XGBoost outperformed the others, achieving an accuracy of 0.75, an area under the receiver operating curve of 0.74 and an area under the precision-recall curve of 0.54. Overall, machine learning-based assessment of the disposition of medium-risk patients according to ESI may lead to predictive models which constitute useful decision support tools.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91903492"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91903492"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91903492; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91903492]").text(description); $(".js-view-count[data-work-id=91903492]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91903492; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91903492']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91903492, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=91903492]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91903492,"title":"AI-driven prediction for the disposition of medium-risk incidents visiting emergency departments","translated_title":"","metadata":{"abstract":"Emergency Departments globally suffer overcrowding due to the lack of adequate capacity and/or guidelines and policies for triaging patients. Medical service quality improvement requires optimal patient prioritization according to their level of urgency. This study aims to evaluate classification models predicting the admission or discharge of incidents triaged as level 3 according to the Emergency Severity Index algorithm. As such, adult patient visits were examined from a publicly available dataset. Feature Importance was used for the assessment of each variable contribution in predictions and a subset of 196 out of 972 variables of the original dataset was sampled. XGBoost, random forest, convolutional neural network, and k-nearest neighbors algorithms were deployed and evaluated regarding hospitalization prediction. Convolutional neural network utilization required a tabular data to image transformation which was applied using the Image Data Generator to Tabular Data (IGTD) algorithm. Benchmarking among the four algorithms showed that XGBoost outperformed the others, achieving an accuracy of 0.75, an area under the receiver operating curve of 0.74 and an area under the precision-recall curve of 0.54. Overall, machine learning-based assessment of the disposition of medium-risk patients according to ESI may lead to predictive models which constitute useful decision support tools.","publisher":"ACM","publication_name":"25th Pan-Hellenic Conference on Informatics"},"translated_abstract":"Emergency Departments globally suffer overcrowding due to the lack of adequate capacity and/or guidelines and policies for triaging patients. Medical service quality improvement requires optimal patient prioritization according to their level of urgency. This study aims to evaluate classification models predicting the admission or discharge of incidents triaged as level 3 according to the Emergency Severity Index algorithm. As such, adult patient visits were examined from a publicly available dataset. Feature Importance was used for the assessment of each variable contribution in predictions and a subset of 196 out of 972 variables of the original dataset was sampled. XGBoost, random forest, convolutional neural network, and k-nearest neighbors algorithms were deployed and evaluated regarding hospitalization prediction. Convolutional neural network utilization required a tabular data to image transformation which was applied using the Image Data Generator to Tabular Data (IGTD) algorithm. Benchmarking among the four algorithms showed that XGBoost outperformed the others, achieving an accuracy of 0.75, an area under the receiver operating curve of 0.74 and an area under the precision-recall curve of 0.54. Overall, machine learning-based assessment of the disposition of medium-risk patients according to ESI may lead to predictive models which constitute useful decision support tools.","internal_url":"https://www.academia.edu/91903492/AI_driven_prediction_for_the_disposition_of_medium_risk_incidents_visiting_emergency_departments","translated_internal_url":"","created_at":"2022-11-29T23:58:41.211-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":3806260,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"AI_driven_prediction_for_the_disposition_of_medium_risk_incidents_visiting_emergency_departments","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":3806260,"first_name":"Antonis","middle_initials":null,"last_name":"Billis","page_name":"AntonisBillis","domain_name":"auth","created_at":"2013-04-15T08:24:11.104-07:00","display_name":"Antonis Billis","url":"https://auth.academia.edu/AntonisBillis"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":92258,"name":"Disposition","url":"https://www.academia.edu/Documents/in/Disposition"}],"urls":[{"id":26538184,"url":"https://dl.acm.org/doi/pdf/10.1145/3503823.3503912"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91903490"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91903490/A_Patient_Oriented_App_ThessHF_to_Improve_Self_Care_Quality_in_Heart_Failure_From_Evidence_Based_Design_to_Pilot_Study"><img alt="Research paper thumbnail of A Patient-Oriented App (ThessHF) to Improve Self-Care Quality in Heart Failure: From Evidence-Based Design to Pilot Study" class="work-thumbnail" src="https://attachments.academia-assets.com/95058934/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91903490/A_Patient_Oriented_App_ThessHF_to_Improve_Self_Care_Quality_in_Heart_Failure_From_Evidence_Based_Design_to_Pilot_Study">A Patient-Oriented App (ThessHF) to Improve Self-Care Quality in Heart Failure: From Evidence-Based Design to Pilot Study</a></div><div class="wp-workCard_item"><span>JMIR mHealth and uHealth</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Background Heart failure (HF) remains a major public health challenge, while HF self-care is part...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Background Heart failure (HF) remains a major public health challenge, while HF self-care is particularly challenging. Mobile health (mHealth)–based interventions taking advantage of smartphone technology have shown particular promise in increasing the quality of self-care among these patients, and in turn improving the outcomes of their disease. Objective The objective of this study was to co-develop with physicians, patients with HF, and their caregivers a patient-oriented mHealth app, perform usability assessment, and investigate its effect on the quality of life of patients with HF and rate of hospitalizations in a pilot study. Methods The development of an mHealth app (The Hellenic Educational Self-care and Support Heart Failure app [ThessHF app]) was evidence based, including features based on previous clinically tested mHealth interventions and selected by a panel of HF expert physicians and discussed with patients with HF. At the end of alpha development, the app was rated b...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b24f3103ece619496a1fb88798159074" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95058934,"asset_id":91903490,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95058934/download_file?st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&st=MTczMjc3OTEwOCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91903490"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91903490"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91903490; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91903490]").text(description); $(".js-view-count[data-work-id=91903490]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91903490; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91903490']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 91903490, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b24f3103ece619496a1fb88798159074" } } $('.js-work-strip[data-work-id=91903490]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91903490,"title":"A Patient-Oriented App (ThessHF) to Improve Self-Care Quality in Heart Failure: From Evidence-Based Design to Pilot Study","translated_title":"","metadata":{"abstract":"Background Heart failure (HF) remains a major public health challenge, while HF self-care is particularly challenging. 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