  ICD(International Classification of Diseases) coding involves assigning ICD
codes to patients visit based on their medical notes. ICD coding is a
challenging multilabel text classification problem due to noisy medical
document inputs. Recent advancements in automated ICD coding have enhanced
performance by integrating additional data and knowledge bases with the
encoding of medical notes and codes. However, most of them ignore the code
hierarchy, leading to improper code assignments. To address these problems, we
propose a novel framework based on associated and hierarchical code description
distillation (AHDD) for better code representation learning and avoidance of
improper code assignment.we utilize the code description and the hierarchical
structure inherent to the ICD codes. Therefore, in this paper, we leverage the
code description and the hierarchical structure inherent to the ICD codes. The
code description is also applied to aware the attention layer and output layer.
Experimental results on the benchmark dataset show the superiority of the
proposed framework over several state-of-the-art baselines.
