Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

ABSTRACT:

Objectives

Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich feature patient data sets more readily available. These large data stores lend themselves to use in modern machine learning (ML) models. This paper investigates the use of transformer-based models to identify critical deterioration in unplanned ED admissions, using free-text fields, such as triage notes, and tabular data, including early warning scores (EWS).

Design

A retrospective ML study.

Setting

A large ED in a UK university teaching hospital.

Methods

We extracted rich feature sets of routine clinical data from the

SUBMITTER: Watson M 

PROVIDER: S-EPMC11624723 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature