Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index.

Authors

Scott Levin
Matthew Toerper
Eric Hamrock
Jeremiah S Hinson
Sean Barnes
Heather Gardner
Andrea Dugas
Bob Linton
Thomas Kirsch, George Washington University
Gabor Kelen

Document Type

Journal Article

Publication Date

5-1-2018

Journal

Annals of emergency medicine

Volume

71

Issue

5

Inclusive Pages

205-210

DOI

10.1097/TA.0000000000001732

Keywords

Adult; Algorithms; Area Under Curve; Cross-Sectional Studies; Emergency Service, Hospital; Female; Humans; Machine Learning; Male; Retrospective Studies; Triage; United States; Vital Signs

APA Citation

Levin, S., Toerper, M., Hamrock, E., Hinson, J., Barnes, S., Gardner, H., Dugas, A., Linton, B., Kirsch, T., & Kelen, G. (2018). Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index.. Annals of emergency medicine, 71 (5). http://dx.doi.org/10.1097/TA.0000000000001732

Peer Reviewed

1