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ICD coding is designed to assign the disease codes to electronic healthrecords (EHRs) upon discharge, which is crucial for billing and clinicalstatistics. In an attempt to improve the effectiveness and efficiency of manualcoding, many methods have been proposed to automatically predict ICD codes fromclinical notes. However, most previous works ignore the decisive informationcontained in structured medical data in EHRs, which is hard to be captured fromthe noisy clinical notes. In this paper, we propose a Tree-enhanced MultimodalAttention Network (TreeMAN) to fuse tabular features and textual features intomultimodal representations by enhancing the text representations withtree-based features via the attention mechanism. Tree-based features areconstructed according to decision trees learned from structured multimodalmedical data, which capture the decisive information about ICD coding. We canapply the same multi-label classifier from previous text models to themultimodal representations to predict ICD codes. Experiments on two MIMICdatasets show that our method outperforms prior state-of-the-art ICD codingapproaches. The code is available at https://github.com/liu-zichen/TreeMAN.

TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding