{"id":3800,"date":"2025-05-06T10:10:10","date_gmt":"2025-05-06T10:10:10","guid":{"rendered":"https:\/\/scientificworld.org\/?p=3800"},"modified":"2025-05-06T10:10:13","modified_gmt":"2025-05-06T10:10:13","slug":"new-explainable-ai-toolkit-predicts-chronic-diseases-years-before-symptoms-appear","status":"publish","type":"post","link":"https:\/\/scientificworld.org\/?p=3800","title":{"rendered":"New Explainable AI Toolkit Predicts Chronic Diseases Years Before Symptoms Appear"},"content":{"rendered":"\n<p>Researchers at the University of Utah have developed RiskPath, an open-source Explainable Artificial Intelligence (XAI) toolkit that can predict chronic diseases years before symptoms emerge. Published by the Department of Psychiatry and Huntsman Mental Health Institute, in <a href=\"http:\/\/dx.doi.org\/10.1016\/j.patter.2025.101240\">Patterns<\/a>, this breakthrough technology analyzes long-term health data with 85-99% accuracy, offering transformative potential for preventive healthcare.<\/p>\n\n\n\n<p>RiskPath represents a major leap forward in disease prediction by using advanced time-series AI algorithms to identify at-risk individuals more accurately than current systems, which often achieve only 50-75% accuracy. Unlike traditional methods, RiskPath not only predicts disease risk but also explains its decisions, helping researchers understand how risk factors evolve.<\/p>\n\n\n\n<p>The toolkit was validated across three large patient cohorts, successfully predicting conditions like depression, anxiety, ADHD, hypertension, and metabolic syndrome. Key advantages include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Enhanced Disease Insights:<\/strong>\u00a0RiskPath maps how risk factors change in importance, revealing critical intervention windows. For example, it identified screen time and executive function as growing risk contributors for ADHD as children age.<\/li>\n\n\n\n<li><strong>Simplified Risk Assessment:<\/strong>\u00a0While capable of analyzing hundreds of variables, RiskPath can predict most conditions using just 10 key factors, making it practical for clinical use.<\/li>\n\n\n\n<li><strong>Clear Visualizations:<\/strong>\u00a0The system provides intuitive visuals highlighting high-risk life stages, aiding in preventive strategy development.<\/li>\n<\/ul>\n\n\n\n<p><br>Dr. Nina de Lacy, lead author of the study, emphasized,&nbsp;<em>&#8220;By identifying high-risk individuals early and pinpointing key risk factors, we can create more targeted preventive strategies. This is crucial for reducing healthcare costs and improving outcomes.&#8221;<\/em><\/p>\n\n\n\n<p>RiskPath\u2019s ability to predict and explain disease risk could revolutionize preventive care, particularly for chronic conditions that dominate healthcare burdens. The team plans to expand its application to more diseases and diverse populations, with potential integration into clinical systems on the horizon.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers at the University of Utah have developed RiskPath, an open-source Explainable Artificial Intelligence (XAI) toolkit that can predict chronic diseases years before symptoms emerge. Published by the Department of Psychiatry and Huntsman Mental Health Institute, in Patterns, this breakthrough technology analyzes long-term health data with 85-99% accuracy, offering transformative potential for preventive healthcare. RiskPath [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1565],"tags":[1592],"class_list":["post-3800","post","type-post","status-publish","format-standard","hentry","category-health-medicine","tag-explainable-deep-learning"],"_links":{"self":[{"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/posts\/3800","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/scientificworld.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3800"}],"version-history":[{"count":1,"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/posts\/3800\/revisions"}],"predecessor-version":[{"id":3801,"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/posts\/3800\/revisions\/3801"}],"wp:attachment":[{"href":"https:\/\/scientificworld.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3800"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/scientificworld.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3800"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/scientificworld.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3800"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}