MHGRL: An Effective Representation Learning Model for Electronic Health Records

Affiliation of Author(s):

School of Informatics, Xiamen University,

Journal:

LREC-COLING 2024

Key Words:

"representation learning";"multimodal heterogeneous graph";"contrastive learning"

Abstract:

Electronic health records (EHRs) serve as a digital repository storing comprehensive medical information about patients. Representation learning for EHRs plays a crucial role in healthcare applications. In this paper, we propose a Multimodal Heterogeneous Graph-enhanced Representation Learning, denoted as MHGRL, aimed at learning effective EHR representations. To address the challenge posed by data insufficiency of EHRs, MHGRL utilizes a multimodal heterogeneous graph to model an EHR. Specifically, we construct a heterogeneous graph for each EHR and enrich it by incorporating multimodal information with medical ontology and textual notes. With the integration of pre-trained model, graph neural network, and attention mechanism, MHGRL effectively incorporates both node attributes and structural information across a multimodal heterogeneous graph. Moreover, we employ contrastive learning to ensure the consistency of representations for similar EHRs and improve the model robustness. The experimental results show that MHGRL outperforms all baselines on two real clinical datasets in downstream tasks, including EHR clustering and disease prediction. The code is available at https://github.com/emmali808/MHGRL.

First Author:

Feiyan Liu

Correspondence Author:

Xiaoli Wang

Co-author:

LiangZhi Li,Chang Liu,Feng Luo,Jinsong Su,Yiming Qian

Indexed by:

Original Research

Discipline:

医疗人工智能 / 计算机科学 / 多模态 / 视觉语言模型

Document Type:

Conference Paper

Translation or Not:

no

Date of Publication:

2024-05-01