A Machine-Learning Based Approach for Predicting Older Adults' Adherence to Technology-Based Cognitive Training.

He, Zhe; Tian, Shubo; Singh, Ankita; Chakraborty, Shayok; Zhang, Shenghao; Lustria, Mia Liza A; Charness, Neil; Roque, Nelson A; Harrell, Erin R; Boot, Walter R

He, Zhe; Tian, Shubo; Singh, Ankita; Chakraborty, Shayok; Zhang, Shenghao; Lustria, Mia Liza A; Charness, Neil; Roque, Nelson A; Harrell, Erin R; Boot, Walter R.

Afiliación

He Z; School of Information, Florida State University, Tallahassee, Florida USA.
Tian S; College of Medicine, Florida State University, Tallahassee, Florida USA.
Singh A; Department of Statistics, Florida State University, Tallahassee, Florida USA.
Chakraborty S; Department of Computer Science, Florida State University, Tallahassee, Florida USA.
Zhang S; Department of Computer Science, Florida State University, Tallahassee, Florida USA.
Lustria MLA; Department of Psychology, Florida State University, Tallahassee, Florida USA.
Charness N; School of Information, Florida State University, Tallahassee, Florida USA.
Roque NA; College of Medicine, Florida State University, Tallahassee, Florida USA.
Harrell ER; Department of Psychology, Florida State University, Tallahassee, Florida USA.
Boot WR; Department of Psychology, University of Central Florida, Orlando, Florida USA.

Inf Process Manag ; 59(5)2022 Sep.

Article en En | MEDLINE | ID: mdl-35909793

Palabras clave

Adherence prediction; Cognitive training; Just-in-time intervention; Machine learning

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Inf Process Manag Año: 2022 Tipo del documento: Article

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