Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department.

ABSTRACT:

Background

Accurate triage is required for efficient allocation of resources and to decrease patients' length of stay. Triage decisions are often subjective and vary by provider, leading to patients being over-triaged or under-triaged. This study developed machine learning models that incorporated natural language processing (NLP) to predict patient disposition. The models were assessed by comparing their performance with the judgements of emergency physicians (EPs).

Method

This retrospective study obtained data from patients visiting EDs between January 2018 and December 2019. Internal validation data came from China Medical University Hospital (CMUH), while external validation data were obtained from Asia University Hospital (AUH). Nontrauma patients aged ≥ 20 years were i

SUBMITTER: Chang YH 

PROVIDER: S-EPMC11657801 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature