{"id":187912,"date":"2012-05-24T00:00:00","date_gmt":"2012-06-19T15:49:44","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/probabilistic-graphical-models-applications-in-biomedicine\/"},"modified":"2016-09-26T08:26:47","modified_gmt":"2016-09-26T15:26:47","slug":"probabilistic-graphical-models-applications-in-biomedicine","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/probabilistic-graphical-models-applications-in-biomedicine\/","title":{"rendered":"Probabilistic Graphical Models: Applications in Biomedicine"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Probabilistic graphical models include a variety of techniques based on probability and decision theory-techniques that give us a theoretically well-founded basis for making decisions under conditions of uncertainty and to solve complex problems efficiently. Over the last year, these methods have been used in a great variety of applications, from medical expert systems to intelligent user interfaces.<\/p>\n<p>In this talk, I give a general introduction to probabilistic graphical models and describe some of the most popular ones, such as Bayesian networks and Markov decision processes. Then I demonstrate their application in three complex problems in biomedicine: (1) helping a physician guide an endoscope in the colon, (2) modeling the evolutionary networks of HIV, and (3) adapting a stroke rehabilitation system for the patient.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Probabilistic graphical models include a variety of techniques based on probability and decision theory-techniques that give us a theoretically well-founded basis for making decisions under conditions of uncertainty and to solve complex problems efficiently. Over the last year, these methods have been used in a great variety of applications, from medical expert systems to intelligent [&hellip;]<\/p>\n","protected":false},"featured_media":196921,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-187912","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/sYP7ydGCZIM","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/187912"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/187912\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/196921"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=187912"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=187912"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=187912"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=187912"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=187912"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=187912"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}