Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning.

ABSTRACT:

Background

Prognostic prediction and the identification of prognostic factors are critical during the early period of atrial-fibrillation (AF)-related strokes as AF is associated with poor outcomes in stroke patients.

Methods

Two independent datasets, namely, the Korean Atrial Fibrillation Evaluation Registry in Ischemic Stroke Patients (K-ATTENTION) and the Korea University Stroke Registry (KUSR), were used for internal and external validation, respectively. These datasets include common variables such as demographic, laboratory, and imaging findings during early hospitalization. Outcomes were unfavorable functional status with modified Rankin scores of 3 or higher and mortality at 3 months. We developed two machine learning models, namely, a tree-based model and a multi-la

SUBMITTER: Jeon ET 

PROVIDER: S-EPMC10663332 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature