Deep‐learning approaches to identify critically Ill patients at emergency department triage using limited information

Abstract

Abstract Study objective Triage quickly identifies critically ill patients, facilitating timely interventions. Many emergency departments (EDs) use emergency severity index (ESI) or abnormal vital sign triggers to guide triage. However, both use fixed thresholds, and false activations are costly. Prior approaches using machinelearning have relied on information that is often unavailable during the triage process. We examined whether deep‐learning approaches could identify critically ill patients only using data immediately available at triage. Methods We conducted a retrospective, cross‐sectional study at an urban tertiary care center, from January 1, 2012–January 1, 2020. De‐identified triage information included structured (age, sex, initial vital signs) and textual (chief complaint) data, with critical illness (mortality or ICU admission within 24 hours) as the outcome. Four progressively complex deep‐learning models were trained and applied to triage information from all patients. We compared the accuracy of the models against ESI as the standard diagnostic test, using area under the receiver‐operator curve (AUC). Results A total of 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% confidence interval [CI] = 0.519–0.522), and ESI <3 demonstrated AUC 0.672 (95% CI = 0.671–0.674), logistic regression classified patients with AUC 0.803 (95% CI = 0.802–0.804), 2‐layer neural network with structured data with AUC 0.811 (95% CI = 0.807–0.815), gradient tree boosting with AUC 0.820 (95% CI = 0.818–0.821), and the neural network model with textual data with AUC 0.851 (95% CI = 0.849–0.852). All successive increases in AUC were statistically significant. Conclusion Deep‐learning techniques represent a promising method of augmenting triage, even with limited information. Further research is needed to determine if improved predictions yield clinical and operational benefits.

Keywords

• clinical decision rules
• critical care
• deep learning
• machine learning
• operations
• triage