Machine learning models predicting undertriage in telephone triage.

ABSTRACT:

Background

Undertriaged patients have worse outcomes than appropriately triaged patients. Machine learning provides better triage prediction than conventional triage in emergency departments, but no machine learning-based undertriage prediction models have yet been developed for prehospital telephone triage. We developed and validated machine learning models for telephone triage.

Materials and methods

We conducted a retrospective cohort study with the largest after-hour house-call (AHHC) service dataset in Japan. Participants were ≥16 years and used the AHHC service between 1 November 2018 and 31 January 2021. We developed five prediction models based on support vector machine (SVM), lasso regression (LR), random forest (RF), gradient-boosted decision tree (XGB), and deep ne

SUBMITTER: Inokuchi R 

PROVIDER: S-EPMC9621252 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature