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This paper is in the following e-collection/theme issue:

AI in Older Adult Care (54) Clinical Informatics (1460) Clinical Information and Decision Making (2110) Machine Learning (2262) Stroke (268)

Published on in Vol 9 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77017, first published .
Machine Learning–Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study

Machine Learning–Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study

Machine Learning–Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study

Authors of this article:

Kang Yuan1 Author Orcid Image ;   Deyan Kong2 Author Orcid Image ;   Jinghui Zhong3 Author Orcid Image ;   Mengdi Xie1 Author Orcid Image ;   Rui Liu1 Author Orcid Image ;   Wen Sun3 Author Orcid Image ;   Xinfeng Liu1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Kang Yuan   1 , PhD ;   Deyan Kong   2 , MD ;   Jinghui Zhong   3 , MD ;   Mengdi Xie   1 , MD ;   Rui Liu   1 , PhD ;   Wen Sun   3 , PhD ;   Xinfeng Liu   1 , PhD

1 Department of Neurology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu Province, China

2 Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China

3 Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China

Corresponding Author:

  • Xinfeng Liu, PhD
  • Department of Neurology
  • Affiliated Jinling Hospital, Medical School of Nanjing University
  • 305 Zhongshan East Road, Xuanwu District
  • Nanjing, Jiangsu Province 210002
  • China
  • Phone: 86 2584801861
  • Fax: 86 2584805169
  • Email: xfliu2@vip.163.com