Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department

Yecheng Liu, Jiandong Gao, Jihai Liu, Joseph Harold Walline, Xiaoying Liu, Ting Zhang, Yunyang Wu, Ji Wu, Huadong Zhu, Weiguo Zhu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation. To derive the MLS, an existing dataset of 22,272 patient encounters from 2012 to 2019 from our institution’s electronic emergency triage system (EETS) was used for algorithm training and validation. The area under the receiver operating characteristic curve (AUC) was 0.875 ± 0.006 (CI:95%) in retrospective dataset using fivefold cross validation, higher than that of reference model (0.843 ± 0.005 (CI:95%)). In the prospective cohort study, compared to the traditional triage system’s 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9%. This MLS method with a real-time explanation for triage officers was able to lower the mis-triage rate of critically ill ED patients.

Original languageEnglish (US)
Article number24044
JournalScientific reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • General

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