A Machine Learning Approach to Support Urgent Stroke Triage Using Administrative Data and Social Determinants of Health at Hospital Presentation: Retrospective Study.

ABSTRACT:

Background

The key to effective stroke management is timely diagnosis and triage. Machine learning (ML) methods developed to assist in detecting stroke have focused on interpreting detailed clinical data such as clinical notes and diagnostic imaging results. However, such information may not be readily available when patients are initially triaged, particularly in rural and underserved communities.

Objective

This study aimed to develop an ML stroke prediction algorithm based on data widely available at the time of patients' hospital presentations and assess the added value of social determinants of health (SDoH) in stroke prediction.

Methods

We conducted a retrospective study of the emergency department and hospitalization records from 2012 to 2014 from all the acute

SUBMITTER: Chen M 

PROVIDER: S-EPMC9926350 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature