Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling
Year: 2024
Type: article
Source: Machine Learning
Authors: Susobhan Ghosh, Raphael Kim, Prasidh Chhabria, Raaz Dwivedi, Predrag Klasnja, Peng Liao, Kelly Zhang, Susan Murphy
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
Funders: National Science Foundation, Kaiser Permanente, DSO National Laboratories - Singapore, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Biomedical Imaging and Bioengineering, National Institute of Dental and Craniofacial Research, Division of Chemical, Bioengineering, Environmental, and Transport Systems
Awards: Foundations of Data Science Institute, G3461793214, Operationalizing Behavioral Theory for mHealth: Dynamics, Context, and Personalization, Graduate Research Fellowship Program (GRFP), G5867770422, AI Institute in Dynamic Systems, Novel use of mHealth data to identify states of vulnerability and receptivity to JITAIs, G7470551772, G7678611020, G8052116646, Foundations of Data Science Institute, Personalized Digital Behavior Change Interventions to Promote Oral Health, Interdisciplinary training: Statistical Genetics/Genomics and Computational Biology, Center for Methodologies for Adapting and Personalizing Prevention, Treatment and Recovery Services for SUD and HIV (MAPS Center), ADAPT: Enabling robust adaptation in mHealth interventions for supporting maintenance of heart-healthy behaviors, mHealth Center for Discovery, Optimization, and Translation of Temporally-Precise Interventions (mDOT)
OpenAlex ID: W4394693772