Emergency department triage prediction of clinical outcomes using machine learning models.

ABSTRACT:

Background

Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI).

Methods

Using National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and d

SUBMITTER: Raita Y 

PROVIDER: S-EPMC6387562 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature