Cargando…
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under...
Autores principales: | Trella, Anna L., Zhang, Kelly W., Nahum-Shani, Inbal, Shetty, Vivek, Doshi-Velez, Finale, Murphy, Susan A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881427/ https://www.ncbi.nlm.nih.gov/pubmed/36713810 http://dx.doi.org/10.3390/a15080255 |
Ejemplares similares
-
MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions
por: Nahum-Shani, Inbal, et al.
Publicado: (2022) -
Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study
por: Militello, Lisa, et al.
Publicado: (2022) -
An interpretable RL framework for pre-deployment modeling in ICU hypotension management
por: Zhang, Kristine, et al.
Publicado: (2022) -
Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables
por: Futoma, Joseph, et al.
Publicado: (2021) -
Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support
por: Nahum-Shani, Inbal, et al.
Publicado: (2017)