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Semantic Message Representations for Behavioral Prediction in a Diabetes Prevention System
Mobile health programs increasingly use machine learning to predict how participants respond to behavioral interventions. However, how message content is represented limits what these models can learn. This thesis studies a diabetes prevention system that sends weekly health messages to participants
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