articleTop 1% cited

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

Annals of Emergency Medicine · 2017 · Vol. 71(5) · pp. 565–574.e2

Scott Levin✉(Johns Hopkins University)Matthew Toerper(Johns Hopkins University)Eric HamrockJeremiah S. Hinson(Johns Hopkins University)Sean Barnes(University of Maryland, Baltimore)Heather Gardner(Johns Hopkins University)Andrea Dugas(Johns Hopkins University)Bob Linton(Johns Hopkins University)Thomas D. KirschGabor D. Kelen(Johns Hopkins University)

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Emergency and Acute Care Studies Trauma and Emergency Care Studies Sepsis Diagnosis and TreatmentTriageMedicineEmergency departmentEmergency medicineMedical emergencyVital signsRetrospective cohort studyPropensity score matchingInternal medicineSurgery

MeSH terms

Machine LearningAdultAlgorithmsCross-Sectional StudiesEmergency Service, HospitalFemaleHumansMaleRetrospective StudiesTriageUnited StatesArea Under CurveVital Signs

Citations

384

FWCI

25.14

field-weighted impact

References

Percentile

100%

vs. same field & year

Citations per year

Cited by

Emergency department triage prediction of clinical outcomes using machine learning models