MHLAT: Multi-hop Label-wise Attention Model for Automatic ICD Coding
Junwen Duan Han Jiang Ying Yu
International Classification of Diseases (ICD) coding is the task of
assigning ICD diagnosis codes to clinical notes. This can be challenging given
the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000
tokens). However, unlike the single-pass reading process in previous works,
humans tend to read the text and label definitions again to get more confident
answers. Moreover, although pretrained language models have been used to
address these problems, they suffer from huge memory usage. To address the
above problems, we propose a simple but effective model called the Multi-Hop
Label-wise ATtention (MHLAT), in which multi-hop label-wise attention is
deployed to get more precise and informative representations. Extensive
experiments on three benchmark MIMIC datasets indicate that our method achieves
significantly better or competitive performance on all seven metrics, with much
fewer parameters to optimize.