16 papers
MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms
Saiyang Feng, Yuanyun Zhang, Shi Li
Foundation models have recently emerged as a powerful paradigm for learning transferable representations from large scale biomedical data, yet existing approaches for physiological…
AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models
Yuanyun Zhang, Shi Li
Recent healthcare foundation models have achieved strong predictive performance through large scale self supervised learning, yet their latent representations frequently entangle p…
WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records
Ruan Dong, Yuanyun Zhang, Shi Li
Representation learning in electronic health records (EHR) has largely followed paradigms inherited from natural language processing, relying on sequence modeling and reconstructio…
Event Fields: Learning Latent Event Structure for Waveform Foundation Models
Li Na, Yuanyun Zhang, Shi Li
We propose a new class of waveform foundation models that departs from conventional sequence based representations by modeling physiological time series as realizations of latent e…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Uncertainty-Aware Foundation Models for Clinical Data
Qian Zhou, Yuanyun Zhang, Shi Li
Healthcare foundation models have largely followed paradigms from natural language processing and computer vision, emphasizing large scale pretraining and deterministic representat…
Discriminative Representation Learning for Clinical Prediction
Yang Zhang, Li Fan, Samuel Lawrence +1
Foundation models in healthcare have largely adopted self supervised pretraining objectives inherited from natural language processing and computer vision, emphasizing reconstructi…
Learning Clinical Representations Under Systematic Distribution Shift
Yuanyun Zhang, Shi Li
Clinical machine learning models are increasingly trained using large scale, multimodal foundation paradigms, yet deployment environments often differ systematically from the data…
Dense Feature Learning via Linear Structure Preservation in Medical Data
Yuanyun Zhang, Mingxuan Zhang, Siyuan Li +4
Deep learning models for medical data are typically trained using task specific objectives that encourage representations to collapse onto a small number of discriminative directio…
Learning Longitudinal Health Representations from EHR and Wearable Data
Yuanyun Zhang, Han Zhou, Li Feng +2
Foundation models trained on electronic health records show strong performance on many clinical prediction tasks but are limited by sparse and irregular documentation. Wearable dev…
Serialized EHR make for good text representations
Zhirong Chou, Quan Qin, Shi Li
The emergence of foundation models in healthcare has opened new avenues for learning generalizable representations from large scale clinical data. Yet, existing approaches often st…
Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support
Wu Hao Ran, Xi Xi, Furong Li +5
The advent of large language models (LLMs) has opened new avenues for analyzing complex, unstructured data, particularly within the medical domain. Electronic Health Records (EHRs)…
Temporal Entailment Pretraining for Clinical Language Models over EHR Data
Tatsunori Tanaka, Fi Zheng, Kai Sato +3
Clinical language models have achieved strong performance on downstream tasks by pretraining on domain specific corpora such as discharge summaries and medical notes. However, most…
ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling
Yuanyun Zhang, Shi Li
The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learning. This paper proposes ChronoFo…
Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers
Shi Li
Deep Learning has emerged as one of the most significant innovations in machine learning. However, a notable limitation of this field lies in the ``black box" decision-making proce…
A Collection of Innovations in Medical AI for patient records in 2024
Yuanyun Zhang, Shi Li
The field of Artificial Intelligence in healthcare is evolving at an unprecedented pace, driven by rapid advancements in machine learning and the recent breakthroughs in large lang…