Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning
This work addresses variability and data scarcity in ICD coding for medical professionals, but it is incremental as it builds on existing methods.
The paper tackled the problem of automatic ICD coding from clinical notes by addressing data variability and limited availability, proposing a contrastive pre-training approach and masked section training that enhanced existing methods' performance.
Automatic coding of International Classification of Diseases (ICD) is a multi-label text categorization task that involves extracting disease or procedure codes from clinical notes. Despite the application of state-of-the-art natural language processing (NLP) techniques, there are still challenges including limited availability of data due to privacy constraints and the high variability of clinical notes caused by different writing habits of medical professionals and various pathological features of patients. In this work, we investigate the semi-structured nature of clinical notes and propose an automatic algorithm to segment them into sections. To address the variability issues in existing ICD coding models with limited data, we introduce a contrastive pre-training approach on sections using a soft multi-label similarity metric based on tree edit distance. Additionally, we design a masked section training strategy to enable ICD coding models to locate sections related to ICD codes. Extensive experimental results demonstrate that our proposed training strategies effectively enhance the performance of existing ICD coding methods.