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An Interpretable Machine Learning Approach to Predict Fall Risk Among Community-Dwelling Older Adults: a Three-Year Longitudinal Study.
Ikeda, Takaaki; Cooray, Upul; Hariyama, Masanori; Aida, Jun; Kondo, Katsunori; Murakami, Masayasu; Osaka, Ken.
Affiliation
  • Ikeda T; Department of Health Policy Science, Graduate School of Medical Science, Yamagata University, Yamagata, Yamagata, Japan. tikeda@med.id.yamagata-u.ac.jp.
  • Cooray U; Department of International and Community Oral Health, Graduate School of Dentistry, Tohoku University, Sendai, Miyagi, Japan. tikeda@med.id.yamagata-u.ac.jp.
  • Hariyama M; Department of International and Community Oral Health, Graduate School of Dentistry, Tohoku University, Sendai, Miyagi, Japan.
  • Aida J; Intelligent Integrated Systems Laboratory, Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan.
  • Kondo K; Department of Oral Health Promotion, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan.
  • Murakami M; Division for Regional Community Development, Liaison Center for Innovative Dentistry, Graduate School of Dentistry, Tohoku University, Sendai, Miyagi, Japan.
  • Osaka K; Department of Social Preventive Medical Sciences, Center for Preventive Medical Sciences, Chiba University, Chiba, Chiba, Japan.
J Gen Intern Med ; 37(11): 2727-2735, 2022 08.
Article in En | MEDLINE | ID: mdl-35112279
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Independent Living / Machine Learning Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Aged / Humans Language: En Journal: J Gen Intern Med Journal subject: MEDICINA INTERNA Year: 2022 Document type: Article Affiliation country: Japan Country of publication: United States
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Independent Living / Machine Learning Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Aged / Humans Language: En Journal: J Gen Intern Med Journal subject: MEDICINA INTERNA Year: 2022 Document type: Article Affiliation country: Japan Country of publication: United States