{"id":16859,"date":"2023-10-01T14:13:11","date_gmt":"2023-10-01T18:13:11","guid":{"rendered":"https:\/\/www.ices.on.ca\/?post_type=journal_article&#038;p=16859"},"modified":"2023-10-18T14:36:49","modified_gmt":"2023-10-18T18:36:49","slug":"machine-learningbased-early-warning-systems","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/machine-learningbased-early-warning-systems\/","title":{"rendered":"Machine learning\u2013based early warning systems for acute care utilization during systemic therapy for cancer"},"content":{"rendered":"<p><strong>Background<\/strong> \u2014 Emergency department visits and hospitalizations frequently occur during systemic therapy for cancer. We developed and evaluated a longitudinal warning system for acute care use.<\/p>\n<p><strong>Methods<\/strong> \u2014 Using a retrospective population-based cohort of patients who started intravenous systemic therapy for nonhematologic cancers between July 1, 2014, and June 30, 2020, we randomly separated patients into cohorts formodel training, hyperparameter tuning andmodel selection, and system testing. Predictive features included static features, such as demographics, cancer type, and treatment regimens, and dynamic features, such as patient-reported symptoms and laboratory values. The longitudinal warning system predicted the probability of acute care utilization within 30 days after each treatment session. Machine learning systemswere developed in the training and tuning cohorts and evaluated in the testing cohort. Sensitivity analyses considered feature importance, other acute care endpoints, and performance within subgroups.<\/p>\n<p><strong>Results<\/strong> \u2014 The cohort included 105,129 patients who received 1,216,385 treatment sessions. Acute care followed 182,444 (15.0%) treatments within 30 days. The ensemble model achieved an area under the receiver operating characteristic curve of 0.742 (95%CI, 0.739\u20130.745) and was well calibrated in the test cohort. Important predictive features included prior acute care use, treatment regimen, and laboratory tests. If the system was set to alarm approximately once every 15 treatments, 25.5% of acute care events would be preceded by an alarm, and 47.4% of patients would experience acute care after an alarm. The system underestimated risk for some treatment regimens and potentially underserved populations such as females and non-English speakers.<\/p>\n<p><strong>Conclusions<\/strong> \u2014 Machine learning warning systems can detect patients at risk for acute care<br \/>\nutilization, which can aid in preventive intervention and facilitate tailored treatment. Future research should address potential biases and prospectively evaluate impact after system deployment.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Background \u2014 Emergency department visits and hospitalizations frequently occur during systemic therapy for cancer. We developed and evaluated a longitudinal warning system for acute care use. Methods \u2014 Using a retrospective population-based cohort of patients who started intravenous systemic therapy for nonhematologic cancers between July 1, 2014, and June 30, 2020, we randomly separated patients [&hellip;]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[24,38,63],"migration-helper-qa-sample-set":[],"class_list":["post-16859","journal_article","type-journal_article","status-publish","hentry","topic-acute-and-emergency-services","topic-cancer-and-cancer-screening","topic-decision-science"],"acf":{"citation":"Grant RC, He JC, Khan F, Liu N, Podolsky S, Kaliwal Y, Powis M, Notta F, Chan KKW, Ghassemi M, Gallinger S, Krzyzanowska MK. <em>J Natl Compr Canc Netw.<\/em> 2023; 21(10):1029\u201337.e21. Epub 2023 Oct 1.","source_url":"https:\/\/doi.org\/10.6004\/jnccn.2023.7046","ices_scientist":[1151,1185,17439],"site":[6733],"research_program":[6741],"news_release":"","journal_article":"","atlas":"","research_report":"","infographic":"","video":"","downloads":null,"links":null,"sitecore_item_id":"","sitecore_item_name":"","sitecore_field_values":"","previous_url":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ICES | Machine learning\u2013based early warning systems for acute care utilization during systemic therapy for cancer<\/title>\n<meta name=\"description\" content=\"Background \u2014 Emergency department visits and hospitalizations frequently occur during systemic therapy for cancer. We developed and evaluated a\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/machine-learningbased-early-warning-systems\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ICES | Machine learning\u2013based early warning systems for acute care utilization during systemic therapy for cancer\" \/>\n<meta property=\"og:description\" content=\"Background \u2014 Emergency department visits and hospitalizations frequently occur during systemic therapy for cancer. 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