  Automatic International Classification of Diseases (ICD) coding aims to
assign multiple ICD codes to a medical note with average length of 3,000+
tokens. This task is challenging due to a high-dimensional space of multi-label
assignment (tens of thousands of ICD codes) and the long-tail challenge: only a
few codes (common diseases) are frequently assigned while most codes (rare
diseases) are infrequently assigned. This study addresses the long-tail
challenge by adapting a prompt-based fine-tuning technique with label
semantics, which has been shown to be effective under few-shot setting. To
further enhance the performance in medical domain, we propose a
knowledge-enhanced longformer by injecting three domain-specific knowledge:
hierarchy, synonym, and abbreviation with additional pretraining using
contrastive learning. Experiments on MIMIC-III-full, a benchmark dataset of
code assignment, show that our proposed method outperforms previous
state-of-the-art method in 14.5% in marco F1 (from 10.3 to 11.8, P<0.001). To
further test our model on few-shot setting, we created a new rare diseases
coding dataset, MIMIC-III-rare50, on which our model improves marco F1 from
17.1 to 30.4 and micro F1 from 17.2 to 32.6 compared to previous method.
