  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.
