Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.

Journal: ESC heart failure
PMID: 38981003

Abstract

AIMS: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data. This study aimed to develop a deep learning-based prediction model for HF rehospitalization within 30, 90, and 365 days after acute HF (AHF) discharge.

Authors

  • Mi-Na Kim
    Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea.
  • Yong Seok Lee
    Data Analytics Group, Samsung SDS, Seoul, Korea.
  • Youngmin Park
    Bio Convergence Research Institute, Bertis Inc., Heungdeok 1-ro, Giheung-gu, Yongin-si, 16954 Gyeonggi-do, Republic of Korea.
  • Ayoung Jung
    Data Analytics Group, Samsung SDS, Seoul, Korea.
  • Hanjee So
    Data Analytics Group, Samsung SDS, Seoul, Korea.
  • Joonwoong Park
    Data Analytics Group, Samsung SDS, Seoul, Korea.
  • Jin-Joo Park
    Department of Internal Medicine, Division of Cardiology, Seoul National University Bundang Hospital, Seongnam, Korea.
  • Dong-Joo Choi
    Department of Internal Medicine, Division of Cardiology, Seoul National University Bundang Hospital, Seongnam, Korea.
  • So-Ree Kim
    Department of Internal Medicine, Division of Cardiology, Anam Hospital, Korea University Medicine, Seoul, Korea.
  • Seong-Mi Park
    Division of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea.

Keywords

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