Analyzing Code Embeddings for Coding Clinical Narratives

Abstract

Medical professionals review clinical narratives to assign medical codes as per the International Classification of Diseases (ICD) for billing and care management. This manual process is inefficient and error-prone as it involves a nuanced one-to-many mapping. Recent works on automated ICD coding learn mappings between low-dimensional representations of the reports and the codes. While they propose novel neural networks for encoding varied types of information about the codes, it is unclear as to what information in the medical codes is helpful for performance improvement and why. Here, we compare different ways to represent, or embed, the codes based on their textual, structural and statistical characteristics, using a single deep learning baseline model in quantitative evaluations on discharge reports from the MIMIC-III Intensive Care Unit database. We also qualitatively analyse the nature of the cases that benefit most from the code embeddings and demonstrate that code embeddings are important for predicting ambiguous and oblique codes.