most citedAn AI Co-Data-Scientist for Prioritizing Candidate Biomarkers from Wearable Sensor Data

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cs.LG2026

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…

cs.LG2026

OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data

Patrick Langer, Thomas Kaar, Max Rosenblattl +19

LLMs have emerged as powerful tools for interpreting multimodal data. In medicine, they hold particular promise for synthesizing large volumes of clinical information into actionab…

cs.LG2025

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…

cs.LG2025

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…

cs.LG2025

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…