10 citations · 18 across the 17 of their papers we have counts for
8 papers · 1 filter
Forecasting Patient Trajectories from the Electronic Health Records with Action-Conditioned World Models
Simon A. Lee, Jiahang Hank Sha, Soomin Chung +3
We introduce a predictive world model for electronic health records (EHR) that represents patient histories as action conditioned latent trajectories. Rather than modeling clinical…
HIPNO: Symmetry-Aware Physics-Informed Neural Operators for Noninvasive Hemodynamic Inference
Yunbei Pan, Jiahang Sha, Simon A. Lee +3
Continuous hemodynamic monitoring guides treatment decisions in surgery and intensive care. However, gold-standard signals are only measured in severe cases due to risks associated…
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…
Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning
Hao Zhou, Simon A. Lee, Cyrus Tanade +12
Biosignals acquired from different locations on the body often provide temporally ordered views of the same underlying physiological process. However, most existing self supervised…
JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures
Ariel Larey, Elay Dahan, Amit Bleiweiss +15
Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature". While effective at capturing local s…
One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models
Zilin Jing, Vincent Jeanselme, Yuta Kobayashi +6
Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory v…
Wavelet-Driven Masked Multiscale Reconstruction for PPG Foundation Models
Megha Thukral, Cyrus Tanade, Simon A. Lee +10
Wearable foundation models have the potential to transform digital health by learning transferable representations from large-scale biosignals collected in everyday settings. While…