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

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 complain

SUBMITTER: Joseph JW 

PROVIDER: S-EPMC7593422 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature