Hierarchical Pretraining on Multimodal Electronic Health Records

Xiaochen Wang, Junyu Luo, Jiaqi Wang, Ziyi Yin, Suhan Cui, Yuan Zhong, Yaqing Wang, Fenglong Ma

Submission Type: Regular Long Paper

Submission Track: NLP Applications

Keywords: Clinical Text, Multimodal Learning, Pretraining, Electronic Health Records

TL;DR: A multimodal pretraining archtecture targeting at Electronic Health Records hierarchy.

Abstract:

Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their generalization capability across diverse downstream tasks using a single pretrained model.
To tackle this challenge, this paper introduces a novel, general, and unified pretraining framework called MedHMP, specifically designed for hierarchically multimodal EHR data. The effectiveness of the proposed MedHMP is demonstrated through experimental results on eight downstream tasks spanning three levels. Comparisons against eighteen baselines further highlight the efficacy of our approach.

Submission Number: 4083