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To aid medical decision making and the attribution of resources, within a few days of admission the identification of patients at high risk of dying in hospital is essential.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used de-identified routine patient data available in the first 2 days of hospitalization in a French University Hospital (between 2016 and 2018) to build models predicting in-hospital mortality (at\u2009\u2265\u20092 and\u2009\u2264\u200930 days after admission). We tested nine different machine learning algorithms with repeated 10-fold cross-validation. Models were trained with 283 variables including age, sex, socio-determinants of health, laboratory test results, procedures (Classification of Medical Acts), medications (Anatomical Therapeutic Chemical code), hospital department\/unit and home address (urban, rural etc.). The models were evaluated using various performance metrics. The dataset contained 123,729 admissions, of which the outcome for 3542 was all-cause in-hospital mortality and 120,187 admissions (no death reported within 30 days) were controls.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The support vector machine, logistic regression and Xgboost algorithms demonstrated high discrimination with a balanced accuracy of 0.81 (95%CI 0.80\u20130.82), 0.82 (95%CI 0.80\u20130.83) and 0.83 (95%CI 0.80\u20130.83) and AUC of 0.90 (95%CI 0.88\u20130.91), 0.90 (95%CI 0.89\u20130.91) and 0.90 (95%CI 0.89\u20130.91) respectively. The most predictive variables for in-hospital mortality in all three models were older age (greater risk), and admission with a confirmed appointment (reduced risk).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We propose three highly discriminating machine-learning models that could improve clinical and organizational decision making for adult patients at hospital admission.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-023-02356-4","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T16:02:10Z","timestamp":1699891330000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Early prediction of in-hospital mortality utilizing multivariate predictive modelling of electronic medical records and socio-determinants of health of the first day of hospitalization"],"prefix":"10.1186","volume":"23","author":[{"given":"Daniel","family":"Stoessel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Fa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana","family":"Artemova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ursula","family":"von Schenck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hadiseh","family":"Nowparast\u00a0Rostami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre-Ephrem","family":"Madiot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"Landelle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fr\u00e9deric","family":"Olive","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alison","family":"Foote","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandre","family":"Moreau-Gaudry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Luc","family":"Bosson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"2356_CR1","unstructured":"French governmental Technical Agency for Information on Hospital Care (ATIH) https:\/\/www.atih.sante.fr\/actualites\/plateforme-des-donnees-hospitalieres. 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