A Comparative Study on Automatic Coding of Medical Letters with Explainability

TL;DR

This paper investigates using NLP and ML techniques with explainability to automate medical letter coding on local computers, demonstrating high accuracy and transparency for clinical application.

Contribution

It introduces a lightweight, explainable ML approach for automatic medical coding suitable for local deployment in clinical settings.

Findings

Models achieved 97.98% usefulness for code prediction

Exploration of ICD and SNOMED CT mapping enhances knowledge integration

Demonstrated feasibility of deploying AI coding tools on local computers

Abstract

This study aims to explore the implementation of Natural Language Processing (NLP) and machine learning (ML) techniques to automate the coding of medical letters with visualised explainability and light-weighted local computer settings. Currently in clinical settings, coding is a manual process that involves assigning codes to each condition, procedure, and medication in a patient's paperwork (e.g., 56265001 heart disease using SNOMED CT code). There are preliminary research on automatic coding in this field using state-of-the-art ML models; however, due to the complexity and size of the models, the real-world deployment is not achieved. To further facilitate the possibility of automatic coding practice, we explore some solutions in a local computer setting; in addition, we explore the function of explainability for transparency of AI models. We used the publicly available MIMIC-III database and the HAN/HLAN network models for ICD code prediction purposes. We also experimented with the mapping between ICD and SNOMED CT knowledge bases. In our experiments, the models provided useful information for 97.98\% of codes. The result of this investigation can shed some light on implementing automatic clinical coding in practice, such as in hospital settings, on the local computers used by clinicians , project page \url{https://github.com/Glenj01/Medical-Coding}.