Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population.

Journal: BMC geriatrics
Published Date: Dec 11, 2023

Abstract

BACKGROUND: Falls impact over 25% of older adults annually, making fall prevention a critical public health focus. We aimed to develop and validate a machine learning-based prediction model for serious fall-related injuries (FRIs) among community-dwelling older adults, incorporating various medication factors.

Authors

  • Kyu-Nam Heo
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea.
  • Jeong Yeon Seok
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea.
  • Young-Mi Ah
    College of Pharmacy, Yeungnam University, Gyeongsan-si, 38541, Republic of Korea.
  • Kwang-Il Kim
    Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea.
  • Seung-Bo Lee
    Department of Brain and Cognitive Engineering, Korea University.
  • Ju-Yeun Lee
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea. jypharm@snu.ac.kr.

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