Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive
  Learning
Chang Lu Chandan K. Reddy Ping Wang Yue Ning
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.