{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:05:52Z","timestamp":1792785952016,"version":"4.5.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100016806","name":"Nantong Natural Science Foundation","doi-asserted-by":"publisher","award":["MS2024060"],"award-info":[{"award-number":["MS2024060"]}],"id":[{"id":"10.13039\/100016806","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nantong University Affiliated Hospital Science and Technology Project","award":["Tfh2603"],"award-info":[{"award-number":["Tfh2603"]}],"id":[{"id":"https:\/\/ror.org\/02afcvw97","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Falls are among the most common adverse events in hospitalized patients, with about 30% leading to injury. We developed a machine learning model to predict which in-hospital fall events would lead to patient injury, thereby supporting post-fall risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed data from 410 patients who had experienced falls at a tertiary general hospital in China. Among them, 134 patients (32.7%) had fall-related injuries. The dataset was divided into training and test sets at a 7:3 ratio by outcome-stratified random sampling. Least absolute shrinkage and selection operator regression was used for feature selection to identify relevant predictors. Four machine learning models\u2014logistic regression, random forest, extreme gradient boosting, and support vector machine\u2014were developed and assessed. Model performance was evaluated in the test set using the area under the receiver operating characteristic curve, Brier score, and calibration curves. According to model performance, the optimal model was selected, and multivariable logistic regression analysis was then performed to determine independent risk factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the test cohort, the logistic regression model showed the strongest predictive ability [area under the receiver operating characteristic curve = 0.863, 95% confidence interval (CI): 0.785\u20130.941; Brier score = 0.142]. Five independent risk factors were detected, including impaired consciousness [odds ratio (OR)\u2009=\u20093.35, 95% CI: 1.58\u20137.10], reduced muscle strength (OR = 3.93, 95% CI: 1.87\u20138.26), use of high-risk medications (OR = 10.07, 95% CI: 5.05\u201320.07), ward-related environmental hazards (OR = 3.30, 95% CI: 1.63\u20136.69), and hypocalcemia (OR = 2.10, 95% CI: 1.05\u20134.19). Based on this model, a nomogram was developed, and decision curve analysis indicated a positive net clinical benefit within the threshold probability range of 0.10\u20130.80.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A prediction model based on five routinely collected clinical variables was developed to estimate fall-related injury risk after an in-hospital fall. The logistic regression model showed a favorable balance among predictive performance, calibration, interpretability, and clinical feasibility. This model may help support post-fall injury risk stratification, triage for further assessment, and monitoring decisions in hospitalized patients who have already experienced a fall.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/intqhc\/mzag128","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T16:49:39Z","timestamp":1787935779000},"source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a machine learning-based model for predicting fall-related injury risk in hospitalized patients"],"prefix":"10.1093","volume":"38","author":[{"given":"Shiyu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Nursing and Rehabilitation, Nantong University , Nantong 226001,","place":["China"]},{"name":"Department of Nursing, Affiliated Hospital of Nantong University , Nantong 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226001,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panpan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Nursing and Rehabilitation, Nantong University , Nantong 226001,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Nursing and Rehabilitation, Nantong University , Nantong 226001,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Nursing, Affiliated Hospital of Nantong University , Nantong 226001,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Nursing, Affiliated Hospital of Nantong University , Nantong 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