{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T03:22:31Z","timestamp":1783480951876,"version":"3.55.0"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T00:00:00Z","timestamp":1608249600000},"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\/100016015","name":"University Hospital Basel","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,18]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Unplanned hospital readmissions are a burden to patients and increase healthcare costs. A wide variety of machine learning (ML) models have been suggested to predict unplanned hospital readmissions. These ML models were often specifically trained on patient populations with certain diseases. However, it is unclear whether these specialized ML models\u2014trained on patient subpopulations with certain diseases or defined by other clinical characteristics\u2014are more accurate than a general ML model trained on an unrestricted hospital cohort. In this study based on an electronic health record cohort of consecutive inpatient cases of a single tertiary care center, we demonstrate that accurate prediction of hospital readmissions may be obtained by general, disease-independent, ML models. This general approach may substantially decrease the cost of development and deployment of respective ML models in daily clinical routine, as all predictions are obtained by the use of a single model.<\/jats:p>","DOI":"10.1093\/jamia\/ocaa299","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T20:10:37Z","timestamp":1605643837000},"page":"868-873","source":"Crossref","is-referenced-by-count":14,"title":["A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions"],"prefix":"10.1093","volume":"28","author":[{"given":"Thomas","family":"Sutter","sequence":"first","affiliation":[{"name":"Medical Data Science, Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"name":"Swiss Institute of Bioinformatics, Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan A","family":"Roth","sequence":"additional","affiliation":[{"name":"University of Basel, Basel, Switzerland"},{"name":"Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Basel, Switzerland"},{"name":"Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Switzerland, Basel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kieran","family":"Chin-Cheong","sequence":"additional","affiliation":[{"name":"Medical Data Science, Department of Computer Science, ETH Zurich, Zurich, Switzerland"},{"name":"Swiss Institute of Bioinformatics, Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4235-1995","authenticated-orcid":false,"given":"Balthasar L","family":"Hug","sequence":"additional","affiliation":[{"name":"University of Basel, Basel, Switzerland"},{"name":"Department of Internal Medicine, Kantonsspital Luzern, Lucerne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia 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