  ICD coding is designed to assign the disease codes to electronic health
records (EHRs) upon discharge, which is crucial for billing and clinical
statistics. In an attempt to improve the effectiveness and efficiency of manual
coding, many methods have been proposed to automatically predict ICD codes from
clinical notes. However, most previous works ignore the decisive information
contained in structured medical data in EHRs, which is hard to be captured from
the noisy clinical notes. In this paper, we propose a Tree-enhanced Multimodal
Attention Network (TreeMAN) to fuse tabular features and textual features into
multimodal representations by enhancing the text representations with
tree-based features via the attention mechanism. Tree-based features are
constructed according to decision trees learned from structured multimodal
medical data, which capture the decisive information about ICD coding. We can
apply the same multi-label classifier from previous text models to the
multimodal representations to predict ICD codes. Experiments on two MIMIC
datasets show that our method outperforms prior state-of-the-art ICD coding
approaches. The code is available at https://github.com/liu-zichen/TreeMAN.
