Anomaly Detection Outperforms Logistic Regression in Predicting Outcomes in Trauma Patients

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dc.contributor.authorZachary Dezman
dc.contributor.authorChen Gao
dc.contributor.authorShiming Yang
dc.contributor.authorPeter Hu
dc.contributor.authorLi Yao
dc.contributor.authorHsiao-Chi Li
dc.contributor.authorChein‐I Chang
dc.contributor.authorColin R. MacKenzie
dc.coverage.spatialBolivia
dc.date.accessioned2026-03-22T14:55:47Z
dc.date.available2026-03-22T14:55:47Z
dc.date.issued2016
dc.descriptionCitaciones: 5
dc.description.abstractAD provides enhanced predictions for clinically relevant outcomes in the trauma patient cohort studied and may assist providers in caring for acutely injured patients in the prehospital arena.
dc.identifier.doi10.1080/10903127.2016.1241327
dc.identifier.urihttps://doi.org/10.1080/10903127.2016.1241327
dc.identifier.urihttps://andeanlibrary.org/handle/123456789/49380
dc.language.isoen
dc.publisherTaylor & Francis
dc.relation.ispartofPrehospital Emergency Care
dc.sourceHigher University of San Andrés
dc.subjectMedicine
dc.subjectLogistic regression
dc.subjectEmergency medicine
dc.subjectAnomaly (physics)
dc.subjectMedical emergency
dc.titleAnomaly Detection Outperforms Logistic Regression in Predicting Outcomes in Trauma Patients
dc.typearticle

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