Evidence map›Paper›PMID 40498430›Full record

ArticleJACC. Asia2025

Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.

Jung-Chi Hsu, Yi-Hsien Hsieh, Yen-Yun Yang, Shu-Lin Chuang, Che Lin, Lian-Yu Lin

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Article in JACC. Asia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jung-Chi HsuDepartment of Internal Medicine, National Taiwan University Hospital Jinshan Branch, New Taipei City, Taiwan; Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
Yi-Hsien HsiehGraduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan.
Yen-Yun YangDepartment of Medical Research, National Taiwan University Hospital, Taipei, Taiwan.
Shu-Lin ChuangDepartment of Medical Research, National Taiwan University Hospital, Taipei, Taiwan.
Che LinGraduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan; Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan; Center for Advanced Computing and Imaging in Biomedicine, National Taiwan University, Taipei, Taiwan; Smart Medicine and Health Informatics Program, National Taiwan University, Taipei, Taiwan. Electronic address: chelin@ntu.edu.tw.
Lian-Yu LinDivision of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan; Smart Medicine and Health Informatics Program, National Taiwan University, Taipei, Taiwan; Cardiovascular Center, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital, Yunlin Branch, Yunlin, Taiwan. Electronic address: hspenos@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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

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

methodsWe 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.

resultsThe annual TIA/IS incidence rate ranged from 181.96 (95% CI: 164.42-200.93) to 15.81 (95% CI: 12.38-20.18) per 1,000 person-years, with an overall incidence of 42.40 (95% CI: 39.60-45.39). The ForeSIIN model achieved the best prediction with an area under the receiver-operating characteristics curve of 0.764 (95% CI: 0.722-0.810), compared with the CHA

conclusionsThe innovative ForeSIIN model demonstrated accurate stroke prediction in AF patients and enhanced the interpretation of dynamic risk factors over time.

Indexed as

atrial fibrillationexplainable artificial intelligencegated recurrent unitrecurrent neural network (RNN)stroke

Identifiers

PMID40498430
PMCPMC12426851

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.