Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM

TL;DR

This paper introduces MINGLE, a novel framework that effectively fuses structured and unstructured EHR data using hypergraph neural networks and large language models, significantly improving predictive accuracy.

Contribution

The paper presents a new multimodal fusion framework for EHR data that combines structural and textual information through a two-level infusion strategy with hypergraph neural networks.

Findings

MINGLE improves predictive performance by 11.83% on average.

Effective integration of semantics and structure enhances EHR data representation.

Framework validated on MIMIC-III and CRADLE datasets.

Abstract

Electronic Health Records (EHRs) have become increasingly popular to support clinical decision-making and healthcare in recent decades. EHRs usually contain heterogeneous information, such as structural data in tabular form and unstructured data in textual notes. Different types of information in EHRs can complement each other and provide a more complete picture of the health status of a patient. While there has been a lot of research on representation learning of structured EHR data, the fusion of different types of EHR data (multimodal fusion) is not well studied. This is mostly because of the complex medical coding systems used and the noise and redundancy present in the written notes. In this work, we propose a new framework called MINGLE, which integrates both structures and semantics in EHR effectively. Our framework uses a two-level infusion strategy to combine medical concept semantics and clinical note semantics into hypergraph neural networks, which learn the complex interactions between different types of data to generate visit representations for downstream prediction. Experiment results on two EHR datasets, the public MIMIC-III and private CRADLE, show that MINGLE can effectively improve predictive performance by 11.83% relatively, enhancing semantic integration as well as multimodal fusion for structural and textual EHR data.