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In mobile health (mHealth), reinforcement learning algorithms that adapt to ones 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 users 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.
Users can be supported to adopt healthy behaviors, such as regular physical activity, via relevant and timely suggestions on their mobile devices. Recently, reinforcement learning algorithms have been found to be effective for learning the optimal co
Synthetic medical data which preserves privacy while maintaining utility can be used as an alternative to real medical data, which has privacy costs and resource constraints associated with it. At present, most models focus on generating cross-sectio
Automatic speech recognition (ASR) systems have dramatically improved over the last few years. ASR systems are most often trained from typical speech, which means that underrepresented groups dont experience the same level of improvement. In this pap
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricte
Predicting pregnancy has been a fundamental problem in womens health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the recent growth of womens health tracking mobile apps offers potential for