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From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
Passively collected behavioral health data from ubiquitous sensors holds significant promise to provide mental health professionals insights from patient's daily lives; however, developing analysis tools to use this data in clinical practice requires addressing challenges of generalization across devices and weak or ambiguous correlations between the measured signals and an individual's mental health.
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.
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