GlyLLM applies pre-trained LLMs to integrate CGM sensor data with structured metadata for glucose forecasting and diabetes categorization, reporting 13.66% lower RMSE and 13.08% higher AUROC than traditional ML on the AI-READI dataset.
DM-Bench: Benchmarking LLMs for personalized decision making in diabetes management
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LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data
GlyLLM applies pre-trained LLMs to integrate CGM sensor data with structured metadata for glucose forecasting and diabetes categorization, reporting 13.66% lower RMSE and 13.08% higher AUROC than traditional ML on the AI-READI dataset.