Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
This addresses the problem of noisy and limited data in mobile health interventions for improving personalized treatment policies, representing a strong specific gain rather than a foundational advancement.
The paper tackles the challenge of personalizing mobile health treatment policies with limited individual data by introducing IntelligentPooling, which adaptively uses data from other users to learn personalized policies, achieving an average of 26% lower regret than state-of-the-art methods across generative models.
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of individuals. Yet the high amount of noise due to the in situ delivery of mHealth interventions can cripple the ability of an algorithm to learn when given access to only a single user's data, making personalization challenging. We present IntelligentPooling, which learns personalized policies via an adaptive, principled use of other users' data. We show that IntelligentPooling achieves an average of 26% lower regret than state-of-the-art across all generative models. Additionally, we inspect the behavior of this approach in a live clinical trial, demonstrating its ability to learn from even a small group of users.