IntelligentPooling: Practical Thompson Sampling for mHealth.

ABSTRACT: In mobile health (mHealth) smart devices deliver behavioral treatments repeatedly over time to a user with the goal of helping the user adopt and maintain healthy behaviors. Reinforcement learning appears ideal for learning how to optimally make these sequential treatment decisions. However, significant challenges must be overcome before reinforcement learning can be effectively deployed in a mobile healthcare setting. In this work we are concerned with the following challenges: 1) individuals who are in the same context can exhibit differential response to treatments 2) only a limited amount of data is available for learning on any one individual, and 3) non-stationary responses to treatment. To address these challenges we generalize Thompson-Sampling bandit algorithms to develop Intellig

SUBMITTER: Tomkins S 

PROVIDER: S-EPMC8494236 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature