Contrastive Learning on Multimodal Analysis of Electronic Health Records

TL;DR

This paper introduces a novel contrastive learning framework for multimodal electronic health records, combining structured and unstructured data to improve patient representation and clinical utility, supported by theoretical analysis and real-world validation.

Contribution

It proposes a new multimodal feature embedding model and contrastive loss for EHR data, with theoretical insights linking it to mutual information and SVD, and demonstrates privacy-preserving capabilities.

Findings

Effective multimodal EHR feature representation achieved

Theoretical connection to mutual information and SVD established

Validated on real-world EHR data with positive clinical utility

Abstract

Electronic health record (EHR) systems contain a wealth of multimodal clinical data including structured data like clinical codes and unstructured data such as clinical notes. However, many existing EHR-focused studies has traditionally either concentrated on an individual modality or merged different modalities in a rather rudimentary fashion. This approach often results in the perception of structured and unstructured data as separate entities, neglecting the inherent synergy between them. Specifically, the two important modalities contain clinically relevant, inextricably linked and complementary health information. A more complete picture of a patient's medical history is captured by the joint analysis of the two modalities of data. Despite the great success of multimodal contrastive learning on vision-language, its potential remains under-explored in the realm of multimodal EHR, particularly in terms of its theoretical understanding. To accommodate the statistical analysis of multimodal EHR data, in this paper, we propose a novel multimodal feature embedding generative model and design a multimodal contrastive loss to obtain the multimodal EHR feature representation. Our theoretical analysis demonstrates the effectiveness of multimodal learning compared to single-modality learning and connects the solution of the loss function to the singular value decomposition of a pointwise mutual information matrix. This connection paves the way for a privacy-preserving algorithm tailored for multimodal EHR feature representation learning. Simulation studies show that the proposed algorithm performs well under a variety of configurations. We further validate the clinical utility of the proposed algorithm in real-world EHR data.