5 citations · 13 across the 14 of their papers we have counts for
17 papers
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…
RubricsTree: Scalable and Evolving Open-Ended Evaluation of Personal Health Agents across Health Memory and Medical Skills
Weizhi Zhang, Zechen Li, Hamid Palangi +16
The LLM-empowered personal health agents with user health (sensor) metrics have offered a promising pathway to alleviate global disparities in healthcare access. However, large-sca…
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…
Towards a General Intelligence and Interface for Wearable Health Data
Girish Narayanswamy, Maxwell A. Xu, A. Ali Heydari +37
While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challeng…
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…
Beyond BMI: Smartphone Body Composition Phenotyping for Cardiometabolic Risk Assessment
Menglian Zhou, Arno Charton, Emily Blanchard +19
Body Mass Index (BMI) is a widely accessible but imprecise proxy of cardiometabolic health. While assessing true body composition is superior, gold-standard methods like Dual-Energ…
An AI Co-Data-Scientist for Prioritizing Candidate Biomarkers from Wearable Sensor Data
Yubin Kim, Salman Rahman, Samuel Schmidgall +33
Wearable devices generate continuous physiological and behavioral data, but converting these signals into clinically reviewable biomarker hypotheses remains labor-intensive. We int…
Cardiovascular-Kidney-Metabolic Health Age Using Wearables and Blood Biomarkers
Zeinab Esmaeilpour, A. Ali Heydari, Daniel McDuff +7
Cardiovascular-Kidney-Metabolic (CKM) syndrome represents a growing public health crisis, yet its subclinical heterogeneity remains underexplored. Early detection of physiological…
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…
The Anatomy of a Personal Health Agent
A. Ali Heydari, Ken Gu, Vidya Srinivas +35
Health is a fundamental pillar of human wellness, and the rapid advancements in large language models (LLMs) have driven the development of a new generation of health agents. Howev…
The Human-AI Hybrid Delphi Model: A Structured Framework for Context-Rich, Expert Consensus in Complex Domains
Cathy Speed, Ahmed A. Metwally
Expert consensus plays a critical role in domains where evidence is complex, conflicting, or insufficient for direct prescription. Traditional methods, such as Delphi studies, cons…
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…
A Scalable Framework for Evaluating Health Language Models
Neil Mallinar, A. Ali Heydari, Xin Liu +10
Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets. Recent studies demonstrate their potential to generate useful, personalized responses wh…
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…