{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T02:09:47Z","timestamp":1767838187121,"version":"3.49.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016545","name":"Roche Diagnostics","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016545","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Like most real-world data, electronic health record (EHR)\u2013derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision support for patient-specific oncology care. Here, we sought to develop a novel ensemble approach to addressing missing data that we term the \u201cmeta-model\u201d and apply the meta-model to patient-specific cancer prognosis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>Using real-world data, we developed a suite of individual random survival forest models to predict survival in patients with advanced lung cancer, colorectal cancer, and breast cancer. Individual models varied by the predictor data used. We combined models for each cancer type into a meta-model that predicted survival for each patient using a weighted mean of the individual models for which the patient had all requisite predictors.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The meta-model significantly outperformed many of the individual models and performed similarly to the best performing individual models. Comparisons of the meta-model to a more traditional imputation-based method of addressing missing data supported the meta-model\u2019s utility.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>We developed a novel machine learning\u2013based strategy to underlie clinical decision support and predict survival in cancer patients, despite missing data. The meta-model may more generally provide a tool for addressing missing data across a variety of clinical prediction problems. Moreover, the meta-model may address other challenges in clinical predictive modeling including model extensibility and integration of predictive algorithms trained across different institutions and datasets.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocaa254","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T20:13:19Z","timestamp":1603829599000},"page":"605-615","source":"Crossref","is-referenced-by-count":10,"title":["Development of a \u201cmeta-model\u201d to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support"],"prefix":"10.1093","volume":"28","author":[{"given":"Jason M","family":"Baron","sequence":"first","affiliation":[{"name":"Independent Consultant, (Somerville, MA) on Behalf of Roche Diagnostics Corporation, Indianapolis, Indiana, USA"},{"name":"Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA"}]},{"given":"Ketan","family":"Paranjape","sequence":"additional","affiliation":[{"name":"Roche Diagnostics Corporation, North America, Indianapolis, Indiana, USA"}]},{"given":"Tara","family":"Love","sequence":"additional","affiliation":[{"name":"Roche Diagnostics Corporation, Santa Clara, California, USA"}]},{"given":"Vishakha","family":"Sharma","sequence":"additional","affiliation":[{"name":"Roche Diagnostics Corporation, Santa Clara, California, USA"}]},{"given":"Denise","family":"Heaney","sequence":"additional","affiliation":[{"name":"Roche Diagnostics Corporation, North America, Indianapolis, Indiana, USA"}]},{"given":"Matthew","family":"Prime","sequence":"additional","affiliation":[{"name":"Roche Diagnostics Corporation, Riehen, Basel Stadt, Switzerland"}]}],"member":"286","published-online":{"date-parts":[[2020,12,1]]},"reference":[{"key":"2021030612311372800_ocaa254-B1","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.cca.2013.09.027","article-title":"The role of informatics and decision support in utilization management","volume":"427","author":"Baron","year":"2014","journal-title":"Clin Chim Acta"},{"issue":"1","key":"2021030612311372800_ocaa254-B2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.4103\/2153-3539.126145","article-title":"The 2013 symposium on pathology data integration and clinical decision support and the current state of field","volume":"5","author":"Baron","year":"2014","journal-title":"J Pathol Inform"},{"issue":"2","key":"2021030612311372800_ocaa254-B3","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.cll.2019.01.010","article-title":"Machine learning and other emerging decision support tools","volume":"39","author":"Baron","year":"2019","journal-title":"Clin Lab Med"},{"issue":"3","key":"2021030612311372800_ocaa254-B4","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1309\/AJCPQIRIB3CT1EJV","article-title":"Detection of preanalytic laboratory testing errors using a statistically guided protocol","volume":"138","author":"Baron","year":"2012","journal-title":"Am J Clin Pathol"},{"issue":"6243","key":"2021030612311372800_ocaa254-B5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1126\/science.aab1328","article-title":"Health care policy. 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