Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making.

ABSTRACT: The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, la

SUBMITTER: Boulitsakis Logothetis S 

PROVIDER: S-EPMC10442440 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature