{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:28:58Z","timestamp":1778826538051,"version":"3.51.4"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T00:00:00Z","timestamp":1607904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["5P30CA124435"],"award-info":[{"award-number":["5P30CA124435"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["National Institutes of Health\/National Center for Research Resources (CTSA"],"award-info":[{"award-number":["National Institutes of Health\/National Center for Research Resources (CTSA"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["UL1 RR025744"],"award-info":[{"award-number":["UL1 RR025744"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Stanford Medicine Program for AI in Healthcare"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Being able to predict a patient\u2019s life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians\u2019 performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>A machine learning model was trained using 14\u00a0600 metastatic cancer patients\u2019 data to predict each patient\u2019s distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015\u20132016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63\u20130.81), 0.77 (0.73\u20130.81), and 0.68 (0.65\u20130.71), respectively.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The machine learning model\u2019s predictions were more accurate than those of the treating physician or a traditional model.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocaa290","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T14:44:46Z","timestamp":1605019486000},"page":"1108-1116","source":"Crossref","is-referenced-by-count":32,"title":["Automated model versus treating physician for predicting survival time of patients with metastatic cancer"],"prefix":"10.1093","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4897-3843","authenticated-orcid":false,"given":"Michael F","family":"Gensheimer","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonya","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathryn R.K","family":"Benson","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Justin N","family":"Carter","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. 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