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

Heo, Kyu-Nam; Seok, Jeong Yeon; Ah, Young-Mi; Kim, Kwang-Il; Lee, Seung-Bo; Lee, Ju-Yeun

Heo, Kyu-Nam; Seok, Jeong Yeon; Ah, Young-Mi; Kim, Kwang-Il; Lee, Seung-Bo; Lee, Ju-Yeun.

Afiliação

Heo KN; College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea.
Seok JY; College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, 1, Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea.
Ah YM; College of Pharmacy, Yeungnam University, Gyeongsan-si, 38541, Republic of Korea.
Kim KI; Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea.
Lee SB; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
Lee JY; Department of Medical Informatics, Keimyung University School of Medicine, Dalgubeol-Daero 1095, Dalseo-Gu, Daegu, 42601, Republic of Korea. koreateam23@gmail.com.

BMC Geriatr ; 23(1): 830, 2023 12 11.

Article em En | MEDLINE | ID: mdl-38082380

Assuntos

Vida Independente; Aprendizado de Máquina; Humanos; Idoso; Fatores de Risco; República da Coreia/epidemiologia

Palavras-chave

Claims data; Fall; Fall-related injury; Machine-learning; Older adults; Prediction model

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Limite: Aged / Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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