An Interpretable Machine Learning Approach to Predict Fall Risk Among Community-Dwelling Older Adults: a Three-Year Longitudinal Study.

Journal: Journal of general internal medicine
Published Date: Feb 2, 2022

Abstract

BACKGROUND: Adverse health effects resulting from falls are a major public health concern. Although studies have identified risk factors for falls, none have examined long-term prediction of fall risk. Furthermore, recent evidence suggests that there are additional risk factors, such as psychosocial factors.

Authors

  • Takaaki Ikeda
    Department of Health Policy Science, Graduate School of Medical Science, Yamagata University, Yamagata, Yamagata, Japan. tikeda@med.id.yamagata-u.ac.jp.
  • Upul Cooray
    Department of International and Community Oral Health, Graduate School of Dentistry, Tohoku University, Sendai, Miyagi, Japan.
  • Masanori Hariyama
    Intelligent Integrated Systems Laboratory, Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan.
  • Jun Aida
    Department of Oral Health Promotion, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan.
  • Katsunori Kondo
    Department of Social Preventive Medical Sciences, Center for Preventive Medical Sciences, Chiba University, Chiba, Chiba, Japan.
  • Masayasu Murakami
    Department of Health Policy Science, Graduate School of Medical Science, Yamagata University, Yamagata, Yamagata, Japan.
  • Ken Osaka
    Department of International and Community Oral Health, Graduate School of Dentistry, Tohoku University, Sendai, Miyagi, Japan.

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