{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:19:39Z","timestamp":1791418779373,"version":"4.3.3"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2019,9,3]],"date-time":"2019-09-03T00:00:00Z","timestamp":1567468800000},"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\/100000092","name":"National Library of Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Predictive analytics have begun to change the workflows of healthcare by giving insight into our future health. Deploying prognostic models into clinical workflows should change behavior and motivate interventions that affect outcomes. As users respond to model predictions, downstream characteristics of the data, including the distribution of the outcome, may change. The ever-changing nature of healthcare necessitates maintenance of prognostic models to ensure their longevity. The more effective a model and intervention(s) are at improving outcomes, the faster a model will appear to degrade. Improving outcomes can disrupt the association between the model\u2019s predictors and the outcome. Model refitting may not always be the most effective response to these challenges. These problems will need to be mitigated by systematically incorporating interventions into prognostic models and by maintaining robust performance surveillance of models in clinical use. Holistically modeling the outcome and intervention(s) can lead to resilience to future compromises in performance.<\/jats:p>","DOI":"10.1093\/jamia\/ocz145","type":"journal-article","created":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T11:25:23Z","timestamp":1563967523000},"page":"1645-1650","source":"Crossref","is-referenced-by-count":88,"title":["Prognostic models will be victims of their own success, unless\u2026"],"prefix":"10.1093","volume":"26","author":[{"given":"Matthew C","family":"Lenert","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael E","family":"Matheny","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA"},{"name":"Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA"},{"name":"Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA"},{"name":"Geriatric Research Education and Clinical Care, Tennessee Valley Health System, Department of Veterans Affairs, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin G","family":"Walsh","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA"},{"name":"Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA"},{"name":"Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,9,3]]},"reference":[{"issue":"10","key":"2020110612454431000_ocz145-B1","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J Am Med Inform Assoc"},{"issue":"11","key":"2020110612454431000_ocz145-B2","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1001\/jama.2018.11029","article-title":"Clinical implications and challenges of artificial intelligence and deep learning","volume":"320","author":"Stead","year":"2018","journal-title":"JAMA"},{"issue":"2","key":"2020110612454431000_ocz145-B3","doi-asserted-by":"crossref","first-page":"b606.","DOI":"10.1136\/bmj.b606","article-title":"Prognosis and prognostic research: application and impact of prognostic models in clinical practice","volume":"338","author":"Moons","year":"2009","journal-title":"BMJ"},{"key":"2020110612454431000_ocz145-B4","author":"Horvitz","year":"2010"},{"key":"2020110612454431000_ocz145-B5","article-title":"Deep learning for healthcare: review, opportunities and challenges","author":"Miotto","journal-title":"Brief Bioinform"},{"key":"2020110612454431000_ocz145-B6","first-page":"304","author":"Hiremath","year":"2014"},{"issue":"1","key":"2020110612454431000_ocz145-B7","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1093\/cid\/cix731","article-title":"Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology","volume":"66","author":"Wiens","year":"2018","journal-title":"Clin Infect Dis"},{"issue":"5","key":"2020110612454431000_ocz145-B8","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1136\/adc.88.5.408","article-title":"ISABEL: a web-based differential diagnostic aid for paediatrics: results from an initial performance evaluation","volume":"88","author":"Ramnarayan","year":"2003","journal-title":"Arch Dis Child"},{"issue":"1","key":"2020110612454431000_ocz145-B9","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1001\/jama.1987.03400010071030","article-title":"DXplain. 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