{"id":23036,"date":"2025-09-25T08:35:34","date_gmt":"2025-09-25T12:35:34","guid":{"rendered":"https:\/\/www.ices.on.ca\/?post_type=journal_article&#038;p=23036"},"modified":"2025-12-17T09:54:47","modified_gmt":"2025-12-17T14:54:47","slug":"development-of-machine-learning-systems-to-predict-cancer-related-symptoms-with-validation-across-a-health-care-system","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/development-of-machine-learning-systems-to-predict-cancer-related-symptoms-with-validation-across-a-health-care-system\/","title":{"rendered":"Development of machine learning systems to predict cancer-related symptoms with validation across a health care system"},"content":{"rendered":"<p><strong>Purpose<\/strong> \u2014 Cancer and its treatment cause symptoms. In this study, we aimed to develop machine learning (ML) systems that predict future symptom deterioration among people receiving treatment for cancer and then validate the systems in a simulated deployment across an entire health care system.<\/p>\n<p><strong>Methods<\/strong> \u2014 We trained and tested ML systems that predict a deterioration in nine patient-reported symptoms within 30 days after treatments for aerodigestive cancers, using internal electronic health record (EHR) data at Princess Margaret Cancer Centre (3,229 patients; 20,267 treatments). The primary performance metric was the area under the receiver operating characteristic curve (AUROC). The best-performing systems in the held-out internal test set were then externally validated across 82 cancer centers in Ontario (12,079 patients; 77,003 treatments) by adapting techniques from meta-analysis.<\/p>\n<p><strong>Results<\/strong> \u2014 The best ML systems predicted symptom deterioration with AUROCs ranging from 0.66 (95% CI, 0.63 to 0.69) for dyspnea to 0.73 (95% CI, 0.71 to 0.75) for drowsiness in the internal test cohort. Treatments flagged as high-risk were significantly associated with future symptom deterioration (odds ratios [ORs], 2.53-6.56; all P &lt; .001) and emergency department visits for dyspnea (OR, 1.85; P = .008), depression (OR, 1.84; P = .04), and anxiety (OR, 2.66; P &lt; .001). In the external validation cohort, the AUROCs for different symptoms meta-analyzed across centers ranged from 0.67 (95% CI, 0.66 to 0.68) to 0.73 (95% CI, 0.72 to 0.74). Performance across centers displayed significant heterogeneity for six of nine symptoms (I2, 46.4%-66.9%; P = .004 for dyspnea, P &lt; .001 for the rest).<\/p>\n<p><strong>Conclusion<\/strong> \u2014 ML can predict future symptoms among people with cancer from routine EHR data, which could guide personalized interventions. Heterogeneous performance across centers must be considered when systems are deployed across a health care system.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Purpose \u2014 Cancer and its treatment cause symptoms. In this study, we aimed to develop machine learning (ML) systems that predict future symptom deterioration among people receiving treatment for cancer and then validate the systems in a simulated deployment across an entire health care system. Methods \u2014 We trained and tested ML systems that predict [&hellip;]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[38,59,61],"migration-helper-qa-sample-set":[],"class_list":["post-23036","journal_article","type-journal_article","status-publish","hentry","topic-cancer-and-cancer-screening","topic-data-science","topic-health-economics"],"acf":{"citation":"Yuan B, Kabir M, He JC, Li Y, Grant B, Narine S, Welch M, Podolsky S, Liu N, Ajaj R, Zhan LJ, Fawzy A, Xu J, Zhang Y, Yu V, Xu W, Krishnan RG, Gallinger S, Chan KKW, Krzyzanowska MK, Truong T, Liu G, Grant RC. <em>JCO Clin Cancer Inform<\/em>. 2025; 9:e2500073. Epub 2025 Sep 25.","source_url":"https:\/\/doi.org\/10.1200\/cci-25-00073","ices_scientist":[1185,1151,17439],"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 | Development of machine learning systems to predict cancer-related symptoms with validation across a health care system<\/title>\n<meta name=\"description\" content=\"Purpose \u2014 Cancer and its treatment cause symptoms. 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