7 papers
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…
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…
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…