CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning

TL;DR

This paper introduces CoRelation, a novel framework that improves automatic ICD coding by modeling code relations within clinical note context, significantly enhancing code representation learning and coding accuracy.

Contribution

It proposes a dependent learning paradigm that effectively captures ICD code relations considering clinical note context, outperforming existing methods.

Findings

Outperforms state-of-the-art baselines on six datasets.

Effectively models complex ICD code relationships.

Enhances ICD code representation learning.

Abstract

Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of automatic ICD coding is modeling ICD code relations. However, current methods insufficiently model the intricate relationships among ICD codes and often overlook the importance of context in clinical notes. In this paper, we propose a novel approach, a contextualized and flexible framework, to enhance the learning of ICD code representations. Our approach, unlike existing methods, employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations. We evaluate our approach on six public ICD coding datasets and the experimental results demonstrate the effectiveness of our approach compared to state-of-the-art baselines.