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

• Published in: Scientific reports Vol. 11; no. 1; p. 24044
• Main Authors: Liu, Yecheng, Gao, Jiandong, Liu, Jihai, Walline, Joseph Harold, Liu, Xiaoying, Zhang, Ting, Wu, Yunyang, Wu, Ji, Zhu, Huadong, Zhu, Weiguo
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 15.12.2021; Nature Publishing Group UK; Nature Portfolio
• Subjects: Adult; Aged; Algorithms; Area Under Curve; Artificial intelligence; Blood pressure; Clinical Decision-Making; Consciousness; Critical care; Critical Care - methods; Critical Care - statistics & numerical data; Critical Illness - therapy; Datasets; Disease Management; Disease Susceptibility; Emergency medical care; Emergency Service, Hospital; Ethics; Female; Hospitals; Humans; Laboratories; Learning algorithms; Machine Learning; Male; Medical schools; Middle Aged; Odds Ratio; Oxygen saturation; Patients; ROC Curve; Triage - methods; Vital signs
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-03104-2

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.