Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review.

ABSTRACT:

Background

The primary objective of this review is to assess the accuracy of machine learning methods in their application of triaging the acuity of patients presenting in the Emergency Care System (ECS). The population are patients that have contacted the ambulance service or turned up at the Emergency Department. The index test is a machine-learning algorithm that aims to stratify the acuity of incoming patients at initial triage. This is in comparison to either an existing decision support tool, clinical opinion or in the absence of these, no comparator. The outcome of this review is the calibration, discrimination and classification statistics.

Methods

Only derivation studies (with or without internal validation) were included. MEDLINE, CINAHL, PubMed and the grey litera

SUBMITTER: Miles J 

PROVIDER: S-EPMC7531169 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature