{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T11:44:44Z","timestamp":1789472684478,"version":"build-2803163510"},"reference-count":32,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ17H260004"],"award-info":[{"award-number":["LQ17H260004"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Wound Journal"],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The study aimed to establish a machine learning\u2013based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (ICU) using large\u2010scale clinical data. A retrospective cohort study design was employed to develop and validate a top\u2010performing clinical feature panel accessibly in the electronic medical records (EMRs), which was in the mode of a quantifiable nomogram. Clinical factors regarding demographics, admission cause, clinical laboratory index, medical history and nursing scales were extracted as risk candidates. The performance improvement was based on the application of the machine learning technique, comprising logistic regression, decision tree and random forest algorithm with five\u2010fold cross\u2010validation (CV) technique. The comprehensive assessment of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC) was considered in the evaluation of predictive performance. The receiver operating characteristic curves revealed the top performance for the logistic regression model in respect to machine learning improvement, achieving the highest sensitivity and AUC among three types of classifiers. Compared against the 23\u2010point Braden scale routinely recorded online, an incorporated nomogram of logistic regression model and Braden scale achieved the best performance with an AUC of 0.87\u2009\u00b1\u20090.07 and 0.84\u2009\u00b1\u20090.05 in training and test cohort, respectively. Our findings suggest that the machine learning technique potentiated the limited predictive validity of routinely recorded clinical data on pressure injury development during ICU hospitalisation. Easily accessible electronic records held the potentials to substitute the traditional Braden score in the prediction of pressure injury in intensive care unit. Preoperative prediction of pressure injury facilitates the exemption from the severe consequences.<\/jats:p>","DOI":"10.1111\/iwj.13764","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T14:03:16Z","timestamp":1643119396000},"page":"1637-1649","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Development and validation of a machine learning <scp>algorithm\u2013based<\/scp> risk prediction model of pressure injury in the intensive care unit"],"prefix":"10.1111","volume":"19","author":[{"given":"Jie","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Thoracic Surgery The First Affiliated Hospital of Wenzhou Medical University  Wenzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danxiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery The First Affiliated Hospital of Wenzhou Medical 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