Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration
Bridging the gap between raw sensor streams and actionable health insights using LLM-based concept integration.
Mobile sensing enables longitudinal monitoring of behavioral and physiological patterns in everyday settings. However, accurate prediction remains challenging in small-cohort health-sensing studies, where task-specific outcome supervision is limited relative to heterogeneous sens...
ArXiv CS.LG
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This research bridges the gap between raw mobile sensor data and clinical interpretation by using LLMs to translate noisy signals into human-readable behavioral concepts. By grounding predictions in expert-defined frameworks, the approach enhances model transparency and reliability in small-cohort health studies.
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