{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T11:55:41Z","timestamp":1765886141821,"version":"3.38.0"},"reference-count":41,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2018,11,29]],"date-time":"2018-11-29T00:00:00Z","timestamp":1543449600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100000724","name":"The Health Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000724","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Institute for Health Research (NIHR) Yorkshire and Humberside Patient Safety Translational Research Centre"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:p> We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients\u2019 first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital ( n\u2009=\u200924,696) and compared the performance of these models in data from another hospital ( n\u2009=\u200913,477). We used two performance measures \u2013 the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well \u2013 calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting. <\/jats:p>","DOI":"10.1177\/1460458218813600","type":"journal-article","created":{"date-parts":[[2018,11,29]],"date-time":"2018-11-29T10:56:01Z","timestamp":1543488961000},"page":"34-44","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":19,"title":["A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation"],"prefix":"10.1177","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4885-4251","authenticated-orcid":false,"given":"Muhammad","family":"Faisal","sequence":"first","affiliation":[]},{"given":"Andy","family":"Scally","sequence":"additional","affiliation":[{"name":"University of Bradford and Bradford Institute for Health Research, UK"}]},{"given":"Robin","family":"Howes","sequence":"additional","affiliation":[{"name":"Northern Lincolnshire and Goole Hospitals NHS Foundation Trust, UK"}]},{"given":"Kevin","family":"Beatson","sequence":"additional","affiliation":[]},{"given":"Donald","family":"Richardson","sequence":"additional","affiliation":[{"name":"York Teaching Hospital NHS Foundation Trust, UK"}]},{"given":"Mohammed A","family":"Mohammed","sequence":"additional","affiliation":[{"name":"University of Bradford and Bradford Institute for Health Research, UK"}]}],"member":"179","published-online":{"date-parts":[[2018,11,29]]},"reference":[{"key":"bibr1-1460458218813600","doi-asserted-by":"publisher","DOI":"10.1097\/00003246-198510000-00009"},{"key":"bibr2-1460458218813600","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1993.03510200084037"},{"key":"bibr3-1460458218813600","doi-asserted-by":"publisher","DOI":"10.1002\/bjs.4041"},{"volume-title":"Clinical prediction models. 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