7 papers
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…