  Leveraging knowledge from electronic health records (EHRs) to predict a
patient's condition is essential to the effective delivery of appropriate care.
Clinical notes of patient EHRs contain valuable information from healthcare
professionals, but have been underused due to their difficult contents and
complex hierarchies. Recently, hypergraph-based methods have been proposed for
document classifications. Directly adopting existing hypergraph methods on
clinical notes cannot sufficiently utilize the hierarchy information of the
patient, which can degrade clinical semantic information by (1) frequent
neutral words and (2) hierarchies with imbalanced distribution. Thus, we
propose a taxonomy-aware multi-level hypergraph neural network (TM-HGNN), where
multi-level hypergraphs assemble useful neutral words with rare keywords via
note and taxonomy level hyperedges to retain the clinical semantic information.
The constructed patient hypergraphs are fed into hierarchical message passing
layers for learning more balanced multi-level knowledge at the note and
taxonomy levels. We validate the effectiveness of TM-HGNN by conducting
extensive experiments with MIMIC-III dataset on benchmark in-hospital-mortality
prediction.
