Predicting unmet activities of daily living needs among the oldest old with disabilities in China: a machine learning approach.

Wang, Kun; Zhao, Jinxu; Hu, Jie; Liang, Dan; Luo, Yansong

Wang, Kun; Zhao, Jinxu; Hu, Jie; Liang, Dan; Luo, Yansong.

Afiliação

Wang K; Zhongnan University of Economics and Law (School of Philosophy), Wuhan, Hubei, China.
Zhao J; Nankai University (Zhou Enlai School of Government), Tianjin, China.
Hu J; Zhongnan University of Economics and Law (School of Philosophy), Wuhan, Hubei, China.
Liang D; Wuhan University (School of Physics and Technology), Wuhan, Hubei, China.
Luo Y; Tongji Medical College of Huazhong University of Science and Technology (School of Medicine and Health Management), Wuhan, Hubei, China.

Front Public Health ; 11: 1257818, 2023.

Article em En | MEDLINE | ID: mdl-37771828

Palavras-chave

ADL; Shap; machine learning; oldest old; unmet needs

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Texto completo: 1 Temas: ECOS / Equidade_desigualdade Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Idioma: En Revista: Front Public Health Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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