The Anatomy of Evidence: An Investigation Into Explainable ICD Coding
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
Automatic medical coding has the potential to ease documentation and billing
processes. For this task, transparency plays an important role for medical
coders and regulatory bodies, which can be achieved using explainability
methods. However, the evaluation of these approaches has been mostly limited to
short text and binary settings due to a scarcity of annotated data. Recent
efforts by Cheng et al. (2023) have introduced the MDACE dataset, which
provides a valuable resource containing code evidence in clinical records. In
this work, we conduct an in-depth analysis of the MDACE dataset and perform
plausibility evaluation of current explainable medical coding systems from an
applied perspective. With this, we contribute to a deeper understanding of
automatic medical coding and evidence extraction. Our findings reveal that
ground truth evidence aligns with code descriptions to a certain degree. An
investigation into state-of-the-art approaches shows a high overlap with ground
truth evidence. We propose match measures and highlight success and failure
cases. Based on our findings, we provide recommendations for developing and
evaluating explainable medical coding systems.
