Personalized Policy Learning Using Longitudinal Mobile Health Data

Xinyu Hu, Min Qian, Bin Cheng & Ying Kuen Cheung
Personalized policy represents a paradigm shift one decision rule for all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications imposes challenges. First, for lack of adherence, data from each user are limited. Second, unmeasured contextual factors can potentially impact on decision making. Aiming to optimize immediate rewards, we propose using a generalized linear mixed modeling framework where population features and individual features are modeled as fixed and...
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