Forecasting the Acute Heart Failure Admissions: Development of Deep Learning Prediction Model Incorporating the Climate Information.

Journal: Journal of cardiac failure
Published Date: Nov 10, 2023

Abstract

BACKGROUND: Climate is known to influence the incidence of cardiovascular events. However, their prediction with traditional statistical models remains imprecise.

Authors

  • Takahiro Jimba
    Tokyo CCU Network Scientific Committee, Tokyo, Japan; Department of Cardiovascular Medicine, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan. Electronic address: blackjtaka@yahoo.co.jp.
  • Satoshi Kodera
    Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo.
  • Shun Kohsaka
    Department of Cardiology, Keio University School of Medicine, Tokyo, Japan.
  • Toshiaki Otsuka
    Tokyo CCU Network Scientific Committee, Tokyo, Japan; Department of Hygiene and Public Health, Nippon Medical School, Tokyo, Japan.
  • Kazumasa Harada
    Department of Cardiology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, 173-0015, Japan.
  • Akito Shindo
    Tokyo CCU Network Scientific Committee, Tokyo, Japan.
  • Yasuyuki Shiraishi
    Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan.
  • Takashi Kohno
    Department of Cardiovascular Medicine, Kyorin University Faculty of Medicine, Tokyo, Japan.
  • Makoto Takei
    Department of Cardiology, Saiseikai Central Hospital, Tokyo, Japan.
  • Hiroki Nakano
    Healthcare Analytics, Global Business Services, IBM Japan Ltd, Tokyo, Japan.
  • Junya Matsuda
    Tokyo CCU Network Scientific Committee, Tokyo, Japan.
  • Takeshi Yamamoto
    Tokyo CCU Network Scientific Committee, Tokyo, Japan.
  • Ken Nagao
    Tokyo CCU Network Scientific Committee, Tokyo, Japan.
  • Morimasa Takayama
    Tokyo CCU Network Scientific Committee, Tokyo, Japan.

Keywords

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