The International Classification of Diseases (ICD) serves as a definitive
medical classification system encompassing a wide range of diseases and
conditions. The primary objective of ICD indexing is to allocate a subset of
ICD codes to a medical record, which facilitates standardized documentation and
management of various health conditions. Most existing approaches have suffered
from selecting the proper label subsets from an extremely large ICD collection
with a heavy long-tailed label distribution. In this paper, we leverage a
multi-stage ``retrieve and re-rank'' framework as a novel solution to ICD
indexing, via a hybrid discrete retrieval method, and re-rank retrieved
candidates with contrastive learning that allows the model to make more
accurate predictions from a simplified label space. The retrieval model is a
hybrid of auxiliary knowledge of the electronic health records (EHR) and a
discrete retrieval method (BM25), which efficiently collects high-quality
candidates. In the last stage, we propose a label co-occurrence guided
contrastive re-ranking model, which re-ranks the candidate labels by pulling
together the clinical notes with positive ICD codes. Experimental results show
the proposed method achieves state-of-the-art performance on a number of
measures on the MIMIC-III benchmark.

本文介绍了一种用于 ICD 编码的多阶段 “检索和重排序” 框架，通过混合离散检索方法和对比学习，在一个简化的标签空间中使模型能够更准确地预测，实验结果显示该方法在 MIMIC-III 基准测试上达到了最先进的性能水平。