Prediction of safety accident subtypes for persons with dementia using sensors and machine learning: an observational study.

Background and Objectives We explored the use of machine learning models for predicting safety accident subtypes among individuals with dementia using in-home sensors and to identify key predictors. Research Design and Methods An observational study was conducted using 966 days of in-home sensor dat...

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Detalles Bibliográficos
Publicado en:	Gerontologist Vol. 66; no. 3; pp. 1 - 11
Autores principales:	Yang, Eunjin, Lee, Ji Yeon, Choi, YeonKyu, Lee, SungHee, Jang, YoonHyung, Cho, Aeyoung, Lee, Kyung Hee
Formato:	Artículo
Publicado:	Oxford University Press / USA Mar2026
Materias:	South Korea Risk-taking behavior Violence Caregivers Self-mutilation Analysis of variance Injury risk factors Accidents Risk assessment Boosting algorithms Prediction models Crush syndrome Electroconvulsive therapy Burns & scalds Research funding Sleep latency Receiver operating characteristic curves Home safety Wandering behavior Invective Scientific observation Questionnaires Logistic regression analysis Wearable technology Respiratory obstructions Foreign bodies Chi-squared test Descriptive statistics Electronic equipment Sleep duration Classification algorithms Support vector machines Prediction algorithms Diary (Literary form) Neuropsychological tests Research methodology Machine learning Data analysis software Comparative studies Dementia patients Time Algorithms Predictive validity Physical activity Accidental falls Motion capture (Human mechanics) Evaluation
Acceso en línea:	Ver este registro en EBSCOhost