Visualizing Medical Coding Practices Using Transformer Models

Tanner Hobson, Jian Huang

2025

Abstract

In the United States, diagnostic codes are a key component of medical records that document the process of patient care. It has long been a common belief that there are inherent orders to the sequences of diagnosis codes in medical records. However, because of the complexities in medical records, there have been few tools that can automatically distill and make sense of the implicit ordering characteristics of the diagnostic codes within medical records. With the advent and fast advancement of the Transformer architecture, in this work we develop and demonstrate a transformer based model named DgPg. DgPg can automatically learn the patterns in the ordering of diagnostic codes in any given corpus of medical records, for example, those obtained from the same hospital or those from different hospitals but collected and organized around particular clinical scenarios. Using DgPg, we can flexibly visualize the coding patterns and context around any particular diagnostic code. Our results from DgPg further demonstrate that the model learned from one dataset can be unique to that dataset and, from this respect, confirm that medical coding practices have unique dependencies on the provider or the clinical scenarios. Our work uses three well known datasets: MIT’s MIMIC-IV dataset, and CDC’s NHDS and NHCS datasets. Our DgPg transformer models are only 2.5 MB in size. Such compact footprint enable flexibility in how the models can be deployed for real world use.

Paper Citation

in Harvard Style

Hobson T. and Huang J. (2025). Visualizing Medical Coding Practices Using Transformer Models. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 725-732. DOI: 10.5220/0013257800003905

in Bibtex Style

@conference{icpram25,
author={Tanner Hobson and Jian Huang},
title={Visualizing Medical Coding Practices Using Transformer Models},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={725-732},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013257800003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Visualizing Medical Coding Practices Using Transformer Models
SN - 978-989-758-730-6
AU - Hobson T.
AU - Huang J.
PY - 2025
SP - 725
EP - 732
DO - 10.5220/0013257800003905
PB - SciTePress