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

Authors

Yikuan Li (ORCID: https://orcid.org/0000-0001-7546-9979)
Kazem Rahimi (ORCID: https://orcid.org/0000-0002-4807-4610)
Mohammad Mamouei (ORCID: https://orcid.org/0000-0002-7757-5238)
Shishir Rao (ORCID: https://orcid.org/0000-0001-7331-9416)
Zhao-chang Fan (ORCID: https://orcid.org/0000-0003-3854-5412)

Institutions

University of Oxford (GB)

Publication Details

Journal: EBioMedicine
Published: 2025-03-19
DOI: https://doi.org/10.1016/j.ebiom.2025.105657
Citations: 5
Primary Topic: Machine Learning in Healthcare
Type: article
Field-Weighted Citation Impact: 12.40

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article

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

Yikuan Li, Kazem Rahimi, Mohammad Mamouei, Shishir Rao et al.

Mar 19, 2025

5 citations

EBioMedicine

Machine Learning in Healthcare

12.40

article

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

Yikuan Li, Kazem Rahimi, Mohammad Mamouei, Shishir Rao, Zhao-chang Fan

March 19, 2025article en

5 citations

Abstract

No abstract available for this paper.

EBioMedicineVol. 114

University of Oxford (GB)

Good health and well-being

Openalex Percentile: Top 2%

Machine Learning in Healthcare

12.40

DOI Open Access PDF OpenAlex

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Identification of heart failure subtypes using transformer-based deep learning modelling: a population-based study of 379,108 individuals — Yikuan Li, Kazem Rahimi, et al. · EBioMedicine (2025) | TGRS Research Map | TGRS