{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:54Z","timestamp":1755219834549,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Electronic health records (EHR) enable machine learning methods like tensor factorization to extract computational phenotypes. Using Northwestern Medicine data (2000\u20132015), we analyzed breast, prostate, colorectal, and lung cancer cohorts to predict five-year mortality. Adding a supervised term, indication filtering, and social determinants of health (SDOH) covariates improved interpretability and performance. AUCs ranged from 0.623\u20130.694 (breast), 0.603\u20130.750 (prostate), 0.523\u20130.641 (colorectal), and 0.517\u20130.623 (lung). Constrained tensor factorization proves effective for deriving mortality-predictive phenotypes from sparse EHR data.<\/jats:p>","DOI":"10.3233\/shti250964","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:36:38Z","timestamp":1754566598000},"source":"Crossref","is-referenced-by-count":0,"title":["Constrained Tensor Factorization for Cancer Phenotyping and Mortality Prediction"],"prefix":"10.3233","author":[{"given":"Francisco Y.","family":"Cai","sequence":"first","affiliation":[{"name":"Feinberg School of Medicine, Northwestern University, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengsheng","family":"Mao","sequence":"additional","affiliation":[{"name":"Feinberg School of Medicine, Northwestern University, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Feinberg School of Medicine, Northwestern University, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250964","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:36:39Z","timestamp":1754566599000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250964"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250964","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}