  Automatic ICD coding is defined as assigning disease codes to electronic
medical records (EMRs). Existing methods usually apply label attention with
code representations to match related text snippets. Unlike these works that
model the label with the code hierarchy or description, we argue that the code
synonyms can provide more comprehensive knowledge based on the observation that
the code expressions in EMRs vary from their descriptions in ICD. By aligning
codes to concepts in UMLS, we collect synonyms of every code. Then, we propose
a multiple synonyms matching network to leverage synonyms for better code
representation learning, and finally help the code classification. Experiments
on the MIMIC-III dataset show that our proposed method outperforms previous
state-of-the-art methods.
