Identification of heart failure subtypes using transformer-based deep learning modelling: a population-based study of 379,108 individuals.

Journal: EBioMedicine
PMID: 40112740

Abstract

BACKGROUND: Heart failure (HF) is a complex syndrome with varied presentations and progression patterns. Traditional classification systems based on left ventricular ejection fraction (LVEF) have limitations in capturing the heterogeneity of HF. We aimed to explore the application of deep learning, specifically a Transformer-based approach, to analyse electronic health records (EHR) for a refined subtyping of patients with HF.

Authors

  • Zhengxian Fan
    Deep Medicine, Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, United Kingdom.
  • Mohammad Mamouei
    Deep Medicine, Oxford Martin School, University of Oxford, Oxford, UK.
  • Yikuan Li
    Department of EECS, Northwestern University, Chicago, IL, U.S.A.
  • Shishir Rao
  • Kazem Rahimi
    Deep Medicine, Oxford Martin School, Oxford, United Kingdom.

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