{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T07:02:32Z","timestamp":1779778952038,"version":"3.53.1"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019331","name":"Henan Medical Science and Technique Foundation","doi-asserted-by":"publisher","award":["232102311084, 242102310046, 252102310224, 252102311157"],"award-info":[{"award-number":["232102311084, 242102310046, 252102310224, 252102311157"]}],"id":[{"id":"10.13039\/501100019331","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Patients with mental disorders are at increased risk of adverse outcomes from COVID-19, but prognostic evidence specific to this population remains limited. This study aimed to develop and validate machine-learning models for predicting 31-day mortality among hospitalized patients with mental disorders and laboratory-confirmed COVID-19.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were retrospectively collected from 439 hospitalized patients across 10 hospitals in Henan Province, China. Patients were randomly divided into a training cohort (n = 308) and an independent test cohort (n = 131). Oversampling was applied during model development to address class imbalance. Candidate predictors were selected using LASSO, Boruta, and random forest methods, and eight machine-learning algorithms were trained. SHAP analysis was used for model interpretation, and Kaplan\u2013Meier analysis compared survival between model-defined risk groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Patients were generally older, 63.1% were female, and comorbidities were common. Several models showed good discrimination in the training cohort, although some showed overfitting. In the test cohort, the neural network model with LASSO-selected features performed best, with an AUC of 0.911 (95% CI: 0.832\u20130.990). SHAP analysis identified concomitant hormone therapy, alkaline phosphatase, and lymphocyte count as the leading predictors. The high-risk group had significantly higher cumulative mortality than the low-risk group (log-rank P &amp;lt; 0.0001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>A machine-learning model based on routine clinical and laboratory variables may support short-term mortality risk stratification in this regional multicenter cohort.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcimb.2026.1815218","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T06:04:53Z","timestamp":1779775493000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a machine learning-based mortality prediction model for patients with mental disorders and 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