5 citations · 13 across the 14 of their papers we have counts for
9 papers · 1 filter
GlucoPRISM: Protocol-Supervised Pretraining for Cross-Cohort Glucose Transfer
Adinath Madhavrao Dukre, Abdullah Mohammad, Yifan Lu +3
Continuous glucose monitoring (CGM) foundation models encode each day as one vector that mixes who the person is, what happened that day and how the sensor measured it, so measurem…
Locating Hidden Failures Makes Long-Horizon Agents More Reliable
Salman Rahman, Yubin Kim, Mihir Parmar +15
As AI agents take on long, autonomous tasks, we increasingly oversee rather than perform the work, yet we still judge them almost entirely by whether they finally succeed. An outco…
GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring
Zechen Li, Keerthana Natarajan, Weizhi Zhang +11
Continuous glucose monitoring (CGM) provides a dense view of daily metabolic physiology, yet existing generic time-series and CGM-specific foundation models often encode glucose tr…
CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
Hada Melino Muhammad, Zechen Li, Flora Salim +1
Continuous Glucose Monitoring (CGM) can detect early metabolic subphenotypes (insulin resistance, IR; -cell dysfunction), but population-scale deployment faces two coupled probl…
HEARTS: Benchmarking LLM Reasoning on Health Time Series
Sirui Li, Shuhan Xiao, Mihir Joshi +4
The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing benchmarks cover only a small set of hea…
SensorLM: Learning the Language of Wearable Sensors
Yuwei Zhang, Kumar Ayush, Siyuan Qiao +17
We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language. Despite its pervasive nature, aligning and…
LSM-2: Learning from Incomplete Wearable Sensor Data
Maxwell A. Xu, Girish Narayanswamy, Kumar Ayush +22
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearable sensor data frequently suffer…
Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers
Ahmed A. Metwally, A. Ali Heydari, Daniel McDuff +9
Insulin resistance, a precursor to type 2 diabetes, is characterized by impaired insulin action in tissues. Current methods for measuring insulin resistance, while effective, are e…
Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning
A. Ali Heydari, Naghmeh Rezaei, Javier L. Prieto +2
Blood biomarkers are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions. Current reference values and recommended ranges…