Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks

TL;DR

This paper introduces a contrastive learning framework that aligns structured EHR data with unstructured clinical texts, significantly improving performance on clinical prediction tasks like hospital readmission.

Contribution

It presents a novel deep multimodal contrastive learning approach that enhances EHR models by integrating unstructured clinical notes for better contextual understanding.

Findings

4.1% AUROC improvement in readmission prediction

Effective alignment of EHR and clinical texts

Enhanced downstream clinical task performance

Abstract

Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction tasks using electronic health record (EHR) data. Despite their strengths, these models struggle with tasks that require deeper contextual understanding, such as predicting 30-day hospital readmission. This can be primarily due to the limited semantic information available in structured EHR data. To address this limitation, we propose a deep multimodal contrastive learning (CL) framework that aligns the latent representations of structured EHR data with unstructured discharge summary notes. It works by pulling together paired EHR and text embeddings while pushing apart unpaired ones. Fine-tuning the pretrained EHR encoder extracted from this framework significantly boosts downstream task performance, e.g., a 4.1% AUROC enhancement over XGBoost for 30-day readmission prediction. Such results demonstrate the effect of integrating domain knowledge from clinical notes into EHR-based pipelines, enabling more accurate and context-aware clinical decision support systems.