Development and Validation of Prediction Models for Incident Reversible Cognitive Frailty Based on Social-Ecological Predictors Using Generalized Linear Mixed Model and Machine Learning Algorithms: A Prospective Cohort Study.

This study aimed to develop and validate prediction models for incident reversible cognitive frailty (RCF) based on social-ecological predictors. Older adults aged ≥60 years from China Health and Retirement Longitudinal Study (CHARLS) 2011–2013 survey were included as training set (n = 1230). The ge...

Descripción completa

Detalles Bibliográficos
Publicado en:	Journal of Applied Gerontology Vol. 44; no. 2; pp. 255 - 267
Autores principales:	Liu, Qinqin, Si, Huaxin, Li, Yanyan, Zhou, Wendie, Yu, Jiaqi, Bian, Yanhui, Wang, Cuili
Formato:	Artículo
Publicado:	Sage Publications Inc. Feb2025
Materias:	China Social factors Old age Cognition disorder risk factors Risk assessment Boosting algorithms Random forest algorithms Prediction models Research funding Frail elderly Longitudinal method Surveys Support vector machines Machine learning Algorithms
Acceso en línea:	Ver este registro en EBSCOhost

Internet

Ver este registro en EBSCOhost