Hierarchical Pretraining on Multimodal Electronic Health Records
This addresses a bottleneck in medical AI by improving generalization for diverse EHR tasks, though it is incremental as it builds on existing pretraining techniques.
The paper tackled the problem that existing pretrained models on electronic health records (EHR) fail to capture hierarchical data, limiting generalization, by introducing MEDHMP, a novel pretraining framework for multimodal EHR, which demonstrated effectiveness on eight downstream tasks across three levels compared to eighteen baselines.
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.