Development of a clinical support system for identifying social frailty.

Journal: International journal of medical informatics
PMID: 31585259

Abstract

OBJECTIVE: Recognizing frailty, also known as clinical geriatric syndrome in the elderly that is characterized by high vulnerability and low resilience, and its extensive influence in clinical practice is challenging. This study aims to develop a social frailty prediction system based on machine learning approaches in order to identify the social frailty status of the elders in order to advance appropriate social services provision.

Authors

  • Kuang-Ming Kuo
    Department of Healthcare Administration, I-Shou University, Kaohsiung City, Taiwan, ROC.
  • Paul C Talley
    Department of Applied English, I-Shou University, No. 1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City, 84001, Taiwan, ROC.
  • Masafumi Kuzuya
    Department of Community Healthcare and Geriatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan; Institutes of Innovation for Future Society, Nagoya University, Nagoya, Japan.
  • Chi-Hsien Huang
    Department of Community Healthcare and Geriatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan; Department of Family Medicine, E-Da Hospital, Kaohsiung City, Taiwan, ROC; School of Medicine for International Students, I-Shou University, Kaohsiung City, Taiwan, ROC. Electronic address: huang.chi.hsien@h.mbox.nagoya-u.ac.jp.

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