  Predicting next visit diagnosis using Electronic Health Records (EHR) is an
essential task in healthcare, critical for devising proactive future plans for
both healthcare providers and patients. Nonetheless, many preceding studies
have not sufficiently addressed the heterogeneous and hierarchical
characteristics inherent in EHR data, inevitably leading to sub-optimal
performance. To this end, we propose NECHO, a novel medical code-centric
multimodal contrastive EHR learning framework with hierarchical regularisation.
First, we integrate multifaceted information encompassing medical codes,
demographics, and clinical notes using a tailored network design and a pair of
bimodal contrastive losses, all of which pivot around a medical codes
representation. We also regularise modality-specific encoders using a parental
level information in medical ontology to learn hierarchical structure of EHR
data. A series of experiments on MIMIC-III data demonstrates effectiveness of
our approach.
