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<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="NZABcEFlrjvE2hW5mNcAjZLh1T6dsIuTAOGH7rJ7ughtm8NE7xAHHzVIoeSQgSXEtvS_TX5ms4GvlXdQbZQF8Q" /> <meta name="citation_title" content="&quot;An Integrated Two-level Hierarchical System for Decision Making in Radiation Therapy using Fuzzy Cognitive Maps&quot;" /> <meta name="citation_journal_title" content="IEEE Transactions on Biomedical Engineering (IF: 1,398) (2003) Vol. 50, No 12, pp. 1326-1339" /> <meta name="citation_author" content="Chrysostomos Stylios" /> <meta name="citation_author" content="Peter Groumpos" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/4194694/_An_Integrated_Two_level_Hierarchical_System_for_Decision_Making_in_Radiation_Therapy_using_Fuzzy_Cognitive_Maps_" /> <meta name="twitter:title" content=""An Integrated Two-level Hierarchical System for Decision Making in Radiation Therapy using Fuzzy Cognitive Maps"" /> <meta name="twitter:description" content="The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the" /> <meta name="twitter:image" content="http://a.academia-assets.com/images/twitter-card.jpeg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/4194694/_An_Integrated_Two_level_Hierarchical_System_for_Decision_Making_in_Radiation_Therapy_using_Fuzzy_Cognitive_Maps_" /> <meta property="og:title" content=""An Integrated Two-level Hierarchical System for Decision Making in Radiation Therapy using Fuzzy Cognitive Maps"" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the" /> <meta property="article:author" content="https://teiep.academia.edu/Chrysostomosstylios" /> <meta property="article:author" content="https://independent.academia.edu/PeterGroumpos" /> <meta name="description" content="The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the" /> <title>(PDF) "An Integrated Two-level Hierarchical System for Decision Making in Radiation Therapy using Fuzzy Cognitive Maps"</title> <link rel="canonical" href="https://www.academia.edu/4194694/_An_Integrated_Two_level_Hierarchical_System_for_Decision_Making_in_Radiation_Therapy_using_Fuzzy_Cognitive_Maps_" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = 'b092bf3a3df71cf13feee7c143e83a57eb6b94fb'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1739831823000); window.Aedu.timeDifference = new Date().getTime() - 1739831823000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision- making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.","author":[{"@context":"https://schema.org","@type":"Person","name":"Chrysostomos Stylios","url":"https://teiep.academia.edu/Chrysostomosstylios"},{"@context":"https://schema.org","@type":"Person","name":"Peter Groumpos","url":"https://independent.academia.edu/PeterGroumpos"}],"contributor":[{"@context":"https://schema.org","@type":"Person","name":"Peter Groumpos","url":"https://independent.academia.edu/PeterGroumpos"}],"dateCreated":"2013-08-07","dateModified":"2017-03-10","headline":"\"An Integrated Two-level Hierarchical System for Decision Making in Radiation Therapy using Fuzzy Cognitive Maps\"","image":"https://attachments.academia-assets.com/31691169/thumbnails/1.jpg","inLanguage":"en","keywords":[],"publication":"IEEE Transactions on Biomedical Engineering (IF: 1,398) (2003) Vol. 50, No 12, pp. 1326-1339","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"teiep"},{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}],"thumbnailUrl":"https://attachments.academia-assets.com/31691169/thumbnails/1.jpg","url":"https://www.academia.edu/4194694/_An_Integrated_Two_level_Hierarchical_System_for_Decision_Making_in_Radiation_Therapy_using_Fuzzy_Cognitive_Maps_"}</script><style type="text/css">@media(max-width: 567px){:root{--token-mode: Rebrand;--dropshadow: 0 2px 4px 0 #22223340;--primary-brand: #0645b1;--error-dark: #b60000;--success-dark: #05b01c;--inactive-fill: #ebebee;--hover: #0c3b8d;--pressed: #082f75;--button-primary-fill-inactive: #ebebee;--button-primary-fill: #0645b1;--button-primary-text: #ffffff;--button-primary-fill-hover: #0c3b8d;--button-primary-fill-press: #082f75;--button-primary-icon: 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"https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F4194694%2F_An_Integrated_Two_level_Hierarchical_System_for_Decision_Making_in_Radiation_Therapy_using_Fuzzy_Cognitive_Maps_%3Fshow_translation%3Dtrue"; window.loswp.previewableAttachments = [{"id":31691169,"identifier":"Attachment_31691169","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":4194694,"created_at":"2013-08-07T19:16:27.218-07:00","from_world_paper_id":null,"updated_at":"2021-01-15T22:43:09.878-08:00","_data":{"abstract":"The radiation therapy decision-making is a complex\r\nprocess that has to take into consideration a variety of interrelated\r\nfunctions. Many fuzzy factors that must be considered in the calculation\r\nof the appropriate dose increase the complexity of the decision-\r\nmaking problem. A novel approach introduces fuzzy cognitive\r\nmaps (FCMs) as the computational modeling method, which\r\ntackles the complexity and allows the analysis and simulation of the\r\nclinical radiation procedure. Specifically this approach is used to\r\ndetermine the success of radiation therapy process estimating the\r\nfinal dose delivered to the target volume, based on the soft computing\r\ntechnique of FCMs. Furthermore a two-level integrated hierarchical\r\nstructure is proposed to supervise and evaluate the radiotherapy\r\nprocess prior to treatment execution. The supervisor\r\ndetermines the treatment variables of cancer therapy and the acceptance\r\nlevel of final radiation dose to the target volume. Two\r\nclinical case studies are used to test the proposed methodology and\r\nevaluate the simulation results. The usefulness of this two-level hierarchical\r\nstructure discussed and future research directions are\r\nsuggested for the clinical use of this methodology.","journal_name":"IEEE Transactions on Biomedical Engineering (IF: 1,398) (2003) Vol. 50, No 12, pp. 1326-1339"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"\"An Integrated Two-level Hierarchical System for Decision Making in Radiation Therapy using Fuzzy Cognitive Maps\"","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [713166,29981354]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":31691169,"attachmentType":"pdf"}"><img alt="First page of “"An Integrated Two-level Hierarchical System for Decision Making in Radiation Therapy using Fuzzy Cognitive Maps"”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/31691169/mini_magick20190426-1465-86tq37.png?1556264088" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">"An Integrated Two-level Hierarchical System for Decision Making in Radiation Therapy using Fuzzy Cognitive Maps"</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios"><img alt="Profile image of Chrysostomos Stylios" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Chrysostomos Stylios</a><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="29981354" href="https://independent.academia.edu/PeterGroumpos"><img alt="Profile image of Peter Groumpos" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/29981354/18756981/18717649/s65_peter.groumpos.png" />Peter Groumpos</a></div><div class="ds-work-card--detail"><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">14 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 4194694; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">The radiation therapy decision-making is a complex process that has to take into consideration a variety of interrelated functions. Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision- making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":31691169,"attachmentType":"pdf","workUrl":"https://www.academia.edu/4194694/_An_Integrated_Two_level_Hierarchical_System_for_Decision_Making_in_Radiation_Therapy_using_Fuzzy_Cognitive_Maps_"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":31691169,"attachmentType":"pdf","workUrl":"https://www.academia.edu/4194694/_An_Integrated_Two_level_Hierarchical_System_for_Decision_Making_in_Radiation_Therapy_using_Fuzzy_Cognitive_Maps_"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Many fuzzy factors that must be considered in the calculation of the appropriate dose increase the complexity of the decision-making problem. A novel approach introduces fuzzy cognitive maps (FCMs) as the computational modeling method, which tackles the complexity and allows the analysis and simulation of the clinical radiation procedure. Specifically this approach is used to determine the success of radiation therapy process estimating the final dose delivered to the target volume, based on the soft computing technique of FCMs. Furthermore a two-level integrated hierarchical structure is proposed to supervise and evaluate the radiotherapy process prior to treatment execution. The supervisor determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Two clinical case studies are used to test the proposed methodology and evaluate the simulation results. The usefulness of this two-level hierarchical structure discussed and future research directions are suggested for the clinical use of this methodology.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps","attachmentId":43369401,"attachmentType":"pdf","work_url":"https://www.academia.edu/22824088/An_integrated_two_level_hierarchical_system_for_decision_making_in_radiation_therapy_based_on_fuzzy_cognitive_maps","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/22824088/An_integrated_two_level_hierarchical_system_for_decision_making_in_radiation_therapy_based_on_fuzzy_cognitive_maps"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="4383739" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4383739/_Decision_Making_in_External_Beam_Radiation_Therapy_based_on_Fuzzy_Cognitive_Maps_">“Decision Making in External Beam Radiation Therapy based on Fuzzy Cognitive Maps”</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><p class="ds-related-work--abstract ds2-5-body-sm">"This work introduces the use of the snft computing technique of F W L ~ Cognitive Maps to model the decision-making process of radiation therapy and develop an advanced system to estimate the delivered dnse to the target volumc. During radiotherapy planning numerous factors are taking into consideration that increase the complexity of the decisiun-making problem. The modeling methodology of FCM ha6 the ability to integrate and consider different, discipline and conflicting factors to determine the dose. A Fuzzy Cognitive Map Model is developed, that can handle imprecise and uncertain information and is used as the decision-making model determining the radiation dose and the complex radiation therapy system. The proposed FCM model is implemented for a practical radiotherapy treatment planning case of gynecological cancer."</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"“Decision Making in External Beam Radiation Therapy based on Fuzzy Cognitive Maps”","attachmentId":31818032,"attachmentType":"pdf","work_url":"https://www.academia.edu/4383739/_Decision_Making_in_External_Beam_Radiation_Therapy_based_on_Fuzzy_Cognitive_Maps_","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4383739/_Decision_Making_in_External_Beam_Radiation_Therapy_based_on_Fuzzy_Cognitive_Maps_"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="4393369" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4393369/_Decision_Support_System_for_radiotherapy_based_on_Fuzzy_Cognitive_Maps_">“Decision Support System for radiotherapy based on Fuzzy Cognitive Maps”</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><p class="ds-related-work--abstract ds2-5-body-sm">During radiotherapy many decisions have to be made and the development of an advanced decision support system that take into consideration different and discipline factors in determining the dose calculation for radiotherapy treatment would be very useful. The implementation of Fuzzy Cognitive Maps (FCM) for decision- making issues in radiotherapy is proposed. FCMs can handle with imprecise, uncertain information and can be used as a decision making model determining radiation dose and other related quantities.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"“Decision Support System for radiotherapy based on Fuzzy Cognitive Maps”","attachmentId":31824285,"attachmentType":"pdf","work_url":"https://www.academia.edu/4393369/_Decision_Support_System_for_radiotherapy_based_on_Fuzzy_Cognitive_Maps_","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4393369/_Decision_Support_System_for_radiotherapy_based_on_Fuzzy_Cognitive_Maps_"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="22824055" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/22824055/A_Fuzzy_Grey_Cognitive_Maps_based_Decision_Support_System_for_radiotherapy_treatment_planning">A Fuzzy Grey Cognitive Maps-based Decision Support System for radiotherapy treatment planning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="7629591" href="https://teiste.academia.edu/ElpinikiPapageorgiou">Elpiniki Papageorgiou</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2012</p><p class="ds-related-work--abstract ds2-5-body-sm">Recently, Fuzzy Grey Cognitive Map (FGCM) has been proposed as a FCM extension. It is based on Grey System Theory, that it is focused on solving problems with high uncertainty, under discrete incomplete and small data sets. The FGCM nodes are variables, representing grey concepts. The relationships between nodes are represented by directed edges. An edge linking two nodes models the grey causal influence of the causal variable on the effect variable. Since FGCMs are hybrid methods mixing Grey Systems and Fuzzy Cognitive Maps, each cause is measured by its grey intensity. An improved construction process of FGCMs is presented in this study, proposing an intensity value to assign the vibration of the grey causal influence, thus to handle the trust of the causal influence on the effect variable initially prescribed by experts' suggestions. The explored methodology is implemented in a well-known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection, where the FCMs have previously proved their usefulness in decision support. Through the examined medical problem, the FGCMs demonstrate their functioning and dynamic capabilities to approximate better human decision making.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Fuzzy Grey Cognitive Maps-based Decision Support System for radiotherapy treatment planning","attachmentId":43369380,"attachmentType":"pdf","work_url":"https://www.academia.edu/22824055/A_Fuzzy_Grey_Cognitive_Maps_based_Decision_Support_System_for_radiotherapy_treatment_planning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/22824055/A_Fuzzy_Grey_Cognitive_Maps_based_Decision_Support_System_for_radiotherapy_treatment_planning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="3069815" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/3069815/Evolutionary_computation_techniques_for_optimizing_fuzzy_cognitive_maps_in_radiation_therapy_systems">Evolutionary computation techniques for optimizing fuzzy cognitive maps in radiation therapy systems</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="1418969" href="https://teilam.academia.edu/ElpinikiPapageorgiou">Elpiniki Papageorgiou</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2004</p><p class="ds-related-work--abstract ds2-5-body-sm">The optimization of a Fuzzy Cognitive Map model for the supervision and monitoring of the radiotherapy process is proposed. This is performed through the minimization of the corresponding objective function by using the Particle Swarm Optimization and the Differential Evolution algorithms. The proposed approach determines the cause–effect relationships among the concepts of the supervisor–Fuzzy Cognitive Map by computing its optimal weight matrix, through extensive experiments. Results are reported and discussed.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Evolutionary computation techniques for optimizing fuzzy cognitive maps in radiation therapy systems","attachmentId":30997053,"attachmentType":"pdf","work_url":"https://www.academia.edu/3069815/Evolutionary_computation_techniques_for_optimizing_fuzzy_cognitive_maps_in_radiation_therapy_systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/3069815/Evolutionary_computation_techniques_for_optimizing_fuzzy_cognitive_maps_in_radiation_therapy_systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="23914224" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/23914224/Fuzzy_modeling_and_optimization_of_planning_in_radiotherapy">Fuzzy modeling and optimization of planning in radiotherapy</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32669728" href="https://independent.academia.edu/NSadati">N. Sadati</a></div><p class="ds-related-work--metadata ds2-5-body-xs">… and Biology Society, 1995 and 14th …, 1995</p><p class="ds-related-work--abstract ds2-5-body-sm">Radiation therapy concerns the delivery of a proper dose of radiation to a tumor volume without causing irreparable damage to, surrounding healthy tissues and critical organs. Radiotherapy planning involves a forward problem and an inverse problem. The ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Fuzzy modeling and optimization of planning in radiotherapy","attachmentId":44301710,"attachmentType":"pdf","work_url":"https://www.academia.edu/23914224/Fuzzy_modeling_and_optimization_of_planning_in_radiotherapy","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/23914224/Fuzzy_modeling_and_optimization_of_planning_in_radiotherapy"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="77162421" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/77162421/A_novel_combination_of_Cased_Based_Reasoning_and_Multi_Criteria_Decision_Making_approach_to_radiotherapy_dose_planning">A novel combination of Cased-Based Reasoning and Multi Criteria Decision Making approach to radiotherapy dose planning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="214475549" href="https://independent.academia.edu/MalekpoorHanif">Hanif Malekpoor</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018</p><p class="ds-related-work--abstract ds2-5-body-sm">In this thesis, a set of novel approaches has been developed by integration of Cased-Based Reasoning (CBR) and Multi-Criteria Decision Making (MCDM) techniques. Its purpose is to design a support system to assist oncologists with decision making about the dose planning for radiotherapy treatment with a focus on radiotherapy for prostate cancer. CBR, an artificial intelligence approach, is a general paradigm to reasoning from past experiences. It retrieves previous cases similar to a new case and exploits the successful past solutions to provide a suggested solution for the new case. The case pool used in this research is a dataset consisting of features and details related to successfully treated patients in Nottingham University Hospital. In a typical run of prostate cancer radiotherapy simple CBR, a new case is selected and thereafter based on the features available at our data set the most similar case to the new case is obtained and its solution is prescribed to the new case. Ho...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A novel combination of Cased-Based Reasoning and Multi Criteria Decision Making approach to radiotherapy dose planning","attachmentId":84605827,"attachmentType":"pdf","work_url":"https://www.academia.edu/77162421/A_novel_combination_of_Cased_Based_Reasoning_and_Multi_Criteria_Decision_Making_approach_to_radiotherapy_dose_planning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/77162421/A_novel_combination_of_Cased_Based_Reasoning_and_Multi_Criteria_Decision_Making_approach_to_radiotherapy_dose_planning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="12460863" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/12460863/A_Fuzzy_Non_linear_Similarity_Measure_for_Case_Based_Reasoning_Systems_for_Radiotherapy_Treatment_Planning">A Fuzzy Non-linear Similarity Measure for Case-Based Reasoning Systems for Radiotherapy Treatment Planning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31288189" href="https://independent.academia.edu/sanjapetrovic4">sanja petrovic</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IFIP Advances in Information and Communication Technology, 2010</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents a decision support system for treatment planning in brain cancer radiotherapy. The aim of a radiotherapy treatment plan is to apply radiation in a way that destroys tumour cells but minimizes the damage to healthy tissue and organs at risk. Treatment planning for brain cancer patients is a complex decision-making process that relies heavily on the subjective experience and expert domain knowledge of clinicians. We propose to capture this experience by using case-based reasoning. Central to the working of our case-based reasoning system is a novel similarity measure that takes into account the non-linear effect of the individual case attributes on the similarity measure. The similarity measure employs fuzzy sets. Experiments, which were carried out to evaluate the similarity measure using real brain cancer patient cases show promising results.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Fuzzy Non-linear Similarity Measure for Case-Based Reasoning Systems for Radiotherapy Treatment Planning","attachmentId":46167760,"attachmentType":"pdf","work_url":"https://www.academia.edu/12460863/A_Fuzzy_Non_linear_Similarity_Measure_for_Case_Based_Reasoning_Systems_for_Radiotherapy_Treatment_Planning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/12460863/A_Fuzzy_Non_linear_Similarity_Measure_for_Case_Based_Reasoning_Systems_for_Radiotherapy_Treatment_Planning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="4394404" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4394404/_Hybrid_model_based_on_Decision_Trees_and_Fuzzy_Cognitive_Maps_for_Medical_Decision_Support_System_">“Hybrid model based on Decision Trees and Fuzzy Cognitive Maps for Medical Decision Support System” </a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><p class="ds-related-work--abstract ds2-5-body-sm">For medical decision making processes (diag- nosing, classification, etc.) all decisions must be made effec- tively and reliably. Conceptual decision making models with the potential of learning capabilities are more appropriate and suitable for performing such hard tasks. Decision trees are a well known technique, which has been applied in many medi- cal systems to support decisions based on a set of instances. On the other hand, the soft computing technique of Fuzzy Cogni- tive Maps (FCMs) is an effective decision making technique, which provides high performance with a conceptual represen- tation of gathered knowledge a nd existing experience. FCMs have been used for medical decision making with emphasis in radiotherapy and classification tasks for bladder tumour grad- ing. This paper proposes and presents an hybrid model de- rived from the combination and the synergistic application of the above mentioned techniques. The proposed Decision Tree- Fuzzy Cognitive Map model has enhanced operation and effec- tiveness based on both methods giving better accuracy results in medical decision tasks.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"“Hybrid model based on Decision Trees and Fuzzy Cognitive Maps for Medical Decision Support System” ","attachmentId":31824908,"attachmentType":"pdf","work_url":"https://www.academia.edu/4394404/_Hybrid_model_based_on_Decision_Trees_and_Fuzzy_Cognitive_Maps_for_Medical_Decision_Support_System_","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4394404/_Hybrid_model_based_on_Decision_Trees_and_Fuzzy_Cognitive_Maps_for_Medical_Decision_Support_System_"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="4195080" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4195080/Complementary_case_based_reasoning_and_competitive_Fuzzy_cognitive_maps_for_advanced_medical_decisions">Complementary case-based reasoning and competitive Fuzzy cognitive maps for advanced medical decisions</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents a new hybrid modeling methodology suitable for complex decision making processes. It extends previous work on competitive fuzzy cognitive maps for medical decision support systems by complementing them with case based reasoning methods. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable advanced medical decision support systems that are suitable to handle situations where the decisions are not clearly distinct. The methodology developed here is applied successfully to model and test two decision support systems, one a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments and the other for decision making choices in external beam radiation therapy.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Complementary case-based reasoning and competitive Fuzzy cognitive maps for advanced medical decisions","attachmentId":31691318,"attachmentType":"pdf","work_url":"https://www.academia.edu/4195080/Complementary_case_based_reasoning_and_competitive_Fuzzy_cognitive_maps_for_advanced_medical_decisions","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4195080/Complementary_case_based_reasoning_and_competitive_Fuzzy_cognitive_maps_for_advanced_medical_decisions"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":31691169,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":31691169,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_31691169" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="31459580" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems">Fuzzy Cognitive Maps Structure for Medical Decision Support Systems</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="713166" href="https://teiep.academia.edu/Chrysostomosstylios">Chrysostomos Stylios</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Fuzzy Cognitive Maps Structure for Medical Decision Support Systems","attachmentId":51816568,"attachmentType":"pdf","work_url":"https://www.academia.edu/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/31459580/Fuzzy_Cognitive_Maps_Structure_for_Medical_Decision_Support_Systems"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="1" data-entity-id="22824118" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/22824118/A_Combined_Fuzzy_Cognitive_Map_and_Decision_Trees_Model_for_Medical_Decision_Making">A Combined Fuzzy Cognitive Map and Decision Trees Model for Medical Decision Making</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="7629591" href="https://teiste.academia.edu/ElpinikiPapageorgiou">Elpiniki Papageorgiou</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Combined Fuzzy Cognitive Map and Decision Trees Model for Medical Decision Making","attachmentId":43369411,"attachmentType":"pdf","work_url":"https://www.academia.edu/22824118/A_Combined_Fuzzy_Cognitive_Map_and_Decision_Trees_Model_for_Medical_Decision_Making","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/22824118/A_Combined_Fuzzy_Cognitive_Map_and_Decision_Trees_Model_for_Medical_Decision_Making"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="2" data-entity-id="314857" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/314857/Interactive_Decision_Support_In_Radiation_Therapy_Treatment_Planning">Interactive Decision Support In Radiation Therapy Treatment Planning</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="68659" href="https://auckland.academia.edu/InesWinz">Ines Winz</a></div><p class="ds-related-work--metadata ds2-5-body-xs">OR Spectrum, 2008</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Interactive Decision Support In Radiation Therapy Treatment Planning","attachmentId":51458872,"attachmentType":"pdf","work_url":"https://www.academia.edu/314857/Interactive_Decision_Support_In_Radiation_Therapy_Treatment_Planning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/314857/Interactive_Decision_Support_In_Radiation_Therapy_Treatment_Planning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="3" data-entity-id="43063229" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/43063229/A_Clinical_Application_of_Fuzzy_Logic">A Clinical Application of Fuzzy Logic</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="158135621" href="https://independent.academia.edu/negarestaniali">ali negarestani</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Fuzzy Logic - Emerging Technologies and Applications, 2012</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Clinical Application of Fuzzy Logic","attachmentId":63323203,"attachmentType":"pdf","work_url":"https://www.academia.edu/43063229/A_Clinical_Application_of_Fuzzy_Logic","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/43063229/A_Clinical_Application_of_Fuzzy_Logic"><span class="ds2-5-text-link__content">View PDF</span><span 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