Prediction of blood pressure variability using deep neural networks.

Journal: International journal of medical informatics
Published Date: Jan 8, 2020

Abstract

PURPOSE: The purpose of our study was to predict blood pressure variability from time-series data of blood pressure measured at home and data obtained through medical examination at a hospital. Previous studies have reported the blood pressure variability is a significant independent risk factor for cardiovascular disease.

Authors

  • Hiroshi Koshimizu
    Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan; Development Center, Omron Healthcare Co., Ltd., Kyoto, 617-0002, Japan. Electronic address: koshimizu.hiroshi.87a@st.kyoto-u.ac.jp.
  • Ryosuke Kojima
    Department of Biomedical Data Intelligence, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, Kyoto, Japan.
  • Kazuomi Kario
    Department of Medicine, Division of Cardiovascular Medicine, Jichi Medical University School of Medicine, Tochigi, Japan.
  • Yasushi Okuno
    Graduate School of Medicine, Kyoto University, Shogoin-kawaharacho, city/>Sakyo-ku Kyoto, 606-8507, Japan.

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