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Emergency department triage prediction of clinical outcomes using machine learning models

Critical Care · 2019 · Vol. 23(1) · pp. 64–64

Yoshihiko Raita✉(Massachusetts General Hospital)Tadahiro Goto(Harvard University)Mohammad Kamal Faridi(Harvard University)David Brown(Harvard University)Carlos A. Camargo(Harvard University)Kohei Hasegawa(Harvard University)

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Abstract

Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.

Emergency and Acute Care Studies Sepsis Diagnosis and Treatment Trauma and Emergency Care StudiesTriageMedicineEmergency departmentReceiver operating characteristicLogistic regressionEmergency medicineMachine learningDecision treeIntensive care unitArtificial intelligence

MeSH terms

Machine LearningAdultEmergency Service, HospitalFemaleForecastingHumansMaleMiddle AgedSurveys and QuestionnairesROC CurveTriageLogistic ModelsHospital MortalityArea Under CurvePatient Outcome Assessment

Citations

494

FWCI

50.83

field-weighted impact

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100%

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Cited by

Optimizing triage systems in emergency care: A comprehensive analysis of patient outcomes and resource allocation