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data-dom-id="Pill-react-component-1bab83cb-0066-445a-a24a-88d62b208722"></div> <div id="Pill-react-component-1bab83cb-0066-445a-a24a-88d62b208722"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="31636764" href="https://www.academia.edu/Documents/in/Reinforcement_Learning"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Reinforcement Learning"]}" data-trace="false" data-dom-id="Pill-react-component-37e42fb6-555e-4fa6-9764-8aa4a83415a4"></div> <div id="Pill-react-component-37e42fb6-555e-4fa6-9764-8aa4a83415a4"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="31636764" href="https://www.academia.edu/Documents/in/Multi-Objective_Optimization"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Multi-Objective 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class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124421599/What_do_you_Mean_The_Role_of_the_Mean_Function_in_Bayesian_Optimisation"><img alt="Research paper thumbnail of What do you Mean? The Role of the Mean Function in Bayesian Optimisation" class="work-thumbnail" src="https://attachments.academia-assets.com/118650141/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/124421599/What_do_you_Mean_The_Role_of_the_Mean_Function_in_Bayesian_Optimisation">What do you Mean? The Role of the Mean Function in Bayesian Optimisation</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Apr 17, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f1f45abf4cb44691a8a809f62a80a991" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118650141,"asset_id":124421599,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118650141/download_file?st=MTczMjQxMDc2MSw4LjIyMi4yMDguMTQ2&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="124421599"><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="124421599"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124421599; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124421599]").text(description); $(".js-view-count[data-work-id=124421599]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124421599; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124421599']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124421599, 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: "f1f45abf4cb44691a8a809f62a80a991" } } $('.js-work-strip[data-work-id=124421599]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124421599,"title":"What do you Mean? 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class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121445663/Using_simulation_and_machine_learning_to_maximise_the_benefit_of_intravenous_thrombolysis_in_acute_stroke_in_England_and_Wales_the_SAMueL_modelling_and_qualitative_study">Using simulation and machine learning to maximise the benefit of intravenous thrombolysis in acute stroke in England and Wales: the SAMueL modelling and qualitative study</a></div><div class="wp-workCard_item"><span>Health and Social Care Delivery Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">BackgroundStroke is a common cause of adult disability. Expert opinion is that about 20% of patie...</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">BackgroundStroke is a common cause of adult disability. Expert opinion is that about 20% of patients should receive thrombolysis to break up a clot causing the stroke. Currently, 11–12% of patients in England and Wales receive this treatment, ranging between 2% and 24% between hospitals.ObjectivesWe sought to enhance the national stroke audit by providing further analysis of the key sources of inter-hospital variation to determine how a target of 20% of stroke patients receiving thrombolysis may be reached.DesignWe modelled three aspects of the thrombolysis pathway, using machine learning and clinical pathway simulation. In addition, the project had a qualitative research arm, with the objective of understanding clinicians’ attitudes to use of modelling and machine learning applied to the national stroke audit.Participants and data sourceAnonymised data were collected for 246,676 emergency stroke admissions to acute stroke teams in England and Wales between 2016 and 2018, obtained f...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a2491d0452ead27f575329242f845a9d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116318449,"asset_id":121445663,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116318449/download_file?st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&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="121445663"><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="121445663"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121445663; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121445663]").text(description); $(".js-view-count[data-work-id=121445663]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121445663; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121445663']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 121445663, 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: "a2491d0452ead27f575329242f845a9d" } } $('.js-work-strip[data-work-id=121445663]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121445663,"title":"Using simulation and machine learning to maximise the benefit of intravenous thrombolysis in acute stroke in England and Wales: the SAMueL modelling and qualitative study","translated_title":"","metadata":{"abstract":"BackgroundStroke is a common cause of adult disability. 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data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/121445662/Can_clinical_audits_be_enhanced_by_pathway_simulation_and_machine_learning_An_example_from_the_acute_stroke_pathway"><img alt="Research paper thumbnail of Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway" class="work-thumbnail" src="https://attachments.academia-assets.com/116318477/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/121445662/Can_clinical_audits_be_enhanced_by_pathway_simulation_and_machine_learning_An_example_from_the_acute_stroke_pathway">Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway</a></div><div class="wp-workCard_item"><span>BMJ Open</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ObjectiveTo evaluate the application of clinical pathway simulation in machine learning, using cl...</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">ObjectiveTo evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals.DesignComputer simulation modelling and machine learning.SettingSeven acute stroke units.ParticipantsAnonymised clinical audit data for 7864 patients.ResultsThree factors were pivotal in governing thrombolysis use: (1) the proportion of patients with a known stroke onset time (range 44%–73%), (2) pathway speed (for patients arriving within 4 hours of onset: per-hospital median arrival-to-scan ranged from 11 to 56 min; median scan-to-thrombolysis ranged from 21 to 44 min) and (3) predisposition to use thrombolysis (thrombolysis use ranged from 31% to 52% for patients with stroke scanned with 30 min left to administer thrombolysis). A pathway simulation model could predict the potential benefit of improving individual stages of the clinical pathway speed, whereas a machin...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="18afd16e8797cf32cefbe82a4efe8190" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116318477,"asset_id":121445662,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116318477/download_file?st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&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="121445662"><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="121445662"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121445662; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121445662]").text(description); $(".js-view-count[data-work-id=121445662]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121445662; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121445662']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 121445662, 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: "18afd16e8797cf32cefbe82a4efe8190" } } $('.js-work-strip[data-work-id=121445662]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121445662,"title":"Can clinical audits be enhanced by pathway simulation and machine learning? 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A pathway simulation model could predict the potential benefit of improving individual stages of the clinical pathway speed, whereas a machin...","publisher":"BMJ","publication_date":{"day":null,"month":null,"year":2019,"errors":{}},"publication_name":"BMJ Open"},"translated_abstract":"ObjectiveTo evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals.DesignComputer simulation modelling and machine learning.SettingSeven acute stroke units.ParticipantsAnonymised clinical audit data for 7864 patients.ResultsThree factors were pivotal in governing thrombolysis use: (1) the proportion of patients with a known stroke onset time (range 44%–73%), (2) pathway speed (for patients arriving within 4 hours of onset: per-hospital median arrival-to-scan ranged from 11 to 56 min; median scan-to-thrombolysis ranged from 21 to 44 min) and (3) predisposition to use thrombolysis (thrombolysis use ranged from 31% to 52% for patients with stroke scanned with 30 min left to administer thrombolysis). 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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="121445647"><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/121445647/A_probabilistic_model_for_aircraft_in_climb_using_monotonic_functional_Gaussian_process_emulators"><img alt="Research paper thumbnail of A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators" class="work-thumbnail" src="https://attachments.academia-assets.com/116318457/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/121445647/A_probabilistic_model_for_aircraft_in_climb_using_monotonic_functional_Gaussian_process_emulators">A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators</a></div><div class="wp-workCard_item"><span>Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Ensuring vertical separation is a key means of maintaining safe separation between aircraft in co...</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">Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. Aircraft trajectories are modelled in the presence of significant epistemic uncertainty, leading to discrepancies between observed trajectories and the predictions of deterministic models, hampering the task of planning to ensure safe separation. In this paper, a probabilistic model is presented, for the purpose of emulating the trajectories of aircraft in climb and bounding the uncertainty of the predicted trajectory. A monotonic, functional representation exploits the spatio-temporal correlations in the radar observations. Through the use of Gaussian process emulators, features that parameterize the climb are mapped directly to functional outputs, providing a fast approximation, while ensuring that the resulting trajectory is monotonic. The model was applied as a probabilistic digital twin for aircraft in climb and baselined against the base of aircraft data, a determ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2e082c3894f659ed8e55a3bc7239e970" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116318457,"asset_id":121445647,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116318457/download_file?st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&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="121445647"><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="121445647"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121445647; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121445647]").text(description); $(".js-view-count[data-work-id=121445647]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121445647; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121445647']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 121445647, 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: "2e082c3894f659ed8e55a3bc7239e970" } } $('.js-work-strip[data-work-id=121445647]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121445647,"title":"A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators","translated_title":"","metadata":{"abstract":"Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. 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The Role of the Mean Function in Bayesian Optimisation</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Apr 17, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f1f45abf4cb44691a8a809f62a80a991" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118650141,"asset_id":124421599,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118650141/download_file?st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQxMDc2MSw4LjIyMi4yMDguMTQ2&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="124421599"><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="124421599"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124421599; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124421599]").text(description); $(".js-view-count[data-work-id=124421599]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124421599; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124421599']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 124421599, 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: "f1f45abf4cb44691a8a809f62a80a991" } } $('.js-work-strip[data-work-id=124421599]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124421599,"title":"What do you Mean? 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class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121445663/Using_simulation_and_machine_learning_to_maximise_the_benefit_of_intravenous_thrombolysis_in_acute_stroke_in_England_and_Wales_the_SAMueL_modelling_and_qualitative_study">Using simulation and machine learning to maximise the benefit of intravenous thrombolysis in acute stroke in England and Wales: the SAMueL modelling and qualitative study</a></div><div class="wp-workCard_item"><span>Health and Social Care Delivery Research</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">BackgroundStroke is a common cause of adult disability. Expert opinion is that about 20% of patie...</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">BackgroundStroke is a common cause of adult disability. Expert opinion is that about 20% of patients should receive thrombolysis to break up a clot causing the stroke. Currently, 11–12% of patients in England and Wales receive this treatment, ranging between 2% and 24% between hospitals.ObjectivesWe sought to enhance the national stroke audit by providing further analysis of the key sources of inter-hospital variation to determine how a target of 20% of stroke patients receiving thrombolysis may be reached.DesignWe modelled three aspects of the thrombolysis pathway, using machine learning and clinical pathway simulation. In addition, the project had a qualitative research arm, with the objective of understanding clinicians’ attitudes to use of modelling and machine learning applied to the national stroke audit.Participants and data sourceAnonymised data were collected for 246,676 emergency stroke admissions to acute stroke teams in England and Wales between 2016 and 2018, obtained f...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a2491d0452ead27f575329242f845a9d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116318449,"asset_id":121445663,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116318449/download_file?st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&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="121445663"><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="121445663"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121445663; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121445663]").text(description); $(".js-view-count[data-work-id=121445663]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121445663; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121445663']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 121445663, 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: "a2491d0452ead27f575329242f845a9d" } } $('.js-work-strip[data-work-id=121445663]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121445663,"title":"Using simulation and machine learning to maximise the benefit of intravenous thrombolysis in acute stroke in England and Wales: the SAMueL modelling and qualitative study","translated_title":"","metadata":{"abstract":"BackgroundStroke is a common cause of adult disability. 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An example from the acute stroke pathway" class="work-thumbnail" src="https://attachments.academia-assets.com/116318477/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/121445662/Can_clinical_audits_be_enhanced_by_pathway_simulation_and_machine_learning_An_example_from_the_acute_stroke_pathway">Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway</a></div><div class="wp-workCard_item"><span>BMJ Open</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ObjectiveTo evaluate the application of clinical pathway simulation in machine learning, using cl...</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">ObjectiveTo evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals.DesignComputer simulation modelling and machine learning.SettingSeven acute stroke units.ParticipantsAnonymised clinical audit data for 7864 patients.ResultsThree factors were pivotal in governing thrombolysis use: (1) the proportion of patients with a known stroke onset time (range 44%–73%), (2) pathway speed (for patients arriving within 4 hours of onset: per-hospital median arrival-to-scan ranged from 11 to 56 min; median scan-to-thrombolysis ranged from 21 to 44 min) and (3) predisposition to use thrombolysis (thrombolysis use ranged from 31% to 52% for patients with stroke scanned with 30 min left to administer thrombolysis). A pathway simulation model could predict the potential benefit of improving individual stages of the clinical pathway speed, whereas a machin...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="18afd16e8797cf32cefbe82a4efe8190" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116318477,"asset_id":121445662,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116318477/download_file?st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&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="121445662"><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="121445662"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121445662; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121445662]").text(description); $(".js-view-count[data-work-id=121445662]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121445662; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121445662']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 121445662, 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: "18afd16e8797cf32cefbe82a4efe8190" } } $('.js-work-strip[data-work-id=121445662]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121445662,"title":"Can clinical audits be enhanced by pathway simulation and machine learning? 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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="121445647"><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/121445647/A_probabilistic_model_for_aircraft_in_climb_using_monotonic_functional_Gaussian_process_emulators"><img alt="Research paper thumbnail of A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators" class="work-thumbnail" src="https://attachments.academia-assets.com/116318457/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/121445647/A_probabilistic_model_for_aircraft_in_climb_using_monotonic_functional_Gaussian_process_emulators">A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators</a></div><div class="wp-workCard_item"><span>Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Ensuring vertical separation is a key means of maintaining safe separation between aircraft in co...</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">Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. Aircraft trajectories are modelled in the presence of significant epistemic uncertainty, leading to discrepancies between observed trajectories and the predictions of deterministic models, hampering the task of planning to ensure safe separation. In this paper, a probabilistic model is presented, for the purpose of emulating the trajectories of aircraft in climb and bounding the uncertainty of the predicted trajectory. A monotonic, functional representation exploits the spatio-temporal correlations in the radar observations. Through the use of Gaussian process emulators, features that parameterize the climb are mapped directly to functional outputs, providing a fast approximation, while ensuring that the resulting trajectory is monotonic. The model was applied as a probabilistic digital twin for aircraft in climb and baselined against the base of aircraft data, a determ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2e082c3894f659ed8e55a3bc7239e970" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116318457,"asset_id":121445647,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116318457/download_file?st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&st=MTczMjQxMDc2Miw4LjIyMi4yMDguMTQ2&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="121445647"><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="121445647"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121445647; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121445647]").text(description); $(".js-view-count[data-work-id=121445647]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121445647; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121445647']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 121445647, 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: "2e082c3894f659ed8e55a3bc7239e970" } } $('.js-work-strip[data-work-id=121445647]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121445647,"title":"A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators","translated_title":"","metadata":{"abstract":"Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. 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