MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models

• URL: http://arxiv.org/abs/2407.12309v1
• Date: Wed, 17 Jul 2024 04:17:09 GMT
• Title: MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models
• Authors: Thao Minh Nguyen Phan, Cong-Tinh Dao, Chenwei Wu, Jian-Zhe Wang, Shun Liu, Jun-En Ding, David Restrepo, Feng Liu, Fang-Ming Hung, Wen-Chih Peng,
• Abstract summary: MEDFuse is a framework that integrates structured and unstructured medical data. It achieves over 90% F1 score in the 10-disease multi-label classification task.
• Score: 11.798375238713488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes. In real-life clinical practice, doctors use complementary multimodal EHR data sources to get a clearer picture of patients' health and support clinical decision-making. However, most EHR predictive models do not reflect these procedures, as they either focus on a single modality or overlook the inter-modality interactions/redundancy. In this work, we propose MEDFuse, a Multimodal EHR Data Fusion framework that incorporates masked lab-test modeling and large language models (LLMs) to effectively integrate structured and unstructured medical data. MEDFuse leverages multimodal embeddings extracted from two sources: LLMs fine-tuned on free clinical text and masked tabular transformers trained on structured lab test results. We design a disentangled transformer module, optimized by a mutual information loss to 1) decouple modality-specific and modality-shared information and 2) extract useful joint representation from the noise and redundancy present in clinical notes. Through comprehensive validation on the public MIMIC-III dataset and the in-house FEMH dataset, MEDFuse demonstrates great potential in advancing clinical predictions, achieving over 90% F1 score in the 10-disease multi-label classification task.

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