Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.

ABSTRACT:

Background

Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.

Methods

A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic heal

SUBMITTER: Maharjan J 

PROVIDER: S-EPMC8823366 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature