Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.

ABSTRACT:

Background

Atrial fibrillation (AF) is a major risk factor for transient ischemic attack (TIA)/ischemic stroke (IS).

Objectives

Given the dynamic nature of IS risk, this study aimed to predict IS risk in AF patients using a high-dimensional time-series model.

Methods

We conducted a cohort study at the National Taiwan University Hospital from 2014 to 2019, including 7,710 AF patients, with external validation in 6,822 patients from the National Taiwan University Hospital Yunlin Branch. The Forecasting Strokes via Interpretable Independent Networks (ForeSIIN) model, based on gated recurrent units, was proposed. Kaplan-Meier analysis with log-rank test evaluated risk group differences.

Results

The annual TIA/IS incidence rate ranged from 181.96 (95% CI: 164.42-200

SUBMITTER: Hsu JC 

PROVIDER: S-EPMC12426851 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature