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    <abstract>Medical claim coding is the process of transforming medical records, usually presented as free texts written by clinicians, or discharge summaries, into structured codes in a classification system such as ICD-10 (International Classification of Diseases, Tenth Revision) or DRG (Diagnosis-Related Group) codes. This process is essential for medical billing and transitional care; however, manual coding is time-consuming, error-prone, and expensive. To solve these issues, we propose DRGCoder, an explainability-enhanced clinical claim coding system for the early prediction of medical severity DRGs (MS-DRGs), a classification system that categorizes patients’ hospital stays into various DRG groups based on the severity of illness and mortality risk. The DRGCoder framework introduces a novel multi-task Transformer model for MS-DRG prediction, modeling both the DRG labels of the discharge summaries and the important, or salient words within he discharge summaries. We allow users to inspect DRGCoder’s reasoning by visualizing the weights for each word of the input. Additionally, DRGCoder allows users to identify diseases within discharge summaries and compare across multiple discharge summaries. Our demo is available at https://huggingface.co/spaces/danielhajialigol/DRGCoder. A video demonstrating the demo can be found at https://www.youtube.com/watch?v=pcdiG6VwqlA</abstract>
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%0 Conference Proceedings
%T DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups
%A Hajialigol, Daniel
%A Kaknes, Derek
%A Barbour, Tanner
%A Yao, Daphne
%A North, Chris
%A Sun, Jimeng
%A Liem, David
%A Wang, Xuan
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hajialigol-etal-2023-drgcoder
%X Medical claim coding is the process of transforming medical records, usually presented as free texts written by clinicians, or discharge summaries, into structured codes in a classification system such as ICD-10 (International Classification of Diseases, Tenth Revision) or DRG (Diagnosis-Related Group) codes. This process is essential for medical billing and transitional care; however, manual coding is time-consuming, error-prone, and expensive. To solve these issues, we propose DRGCoder, an explainability-enhanced clinical claim coding system for the early prediction of medical severity DRGs (MS-DRGs), a classification system that categorizes patients’ hospital stays into various DRG groups based on the severity of illness and mortality risk. The DRGCoder framework introduces a novel multi-task Transformer model for MS-DRG prediction, modeling both the DRG labels of the discharge summaries and the important, or salient words within he discharge summaries. We allow users to inspect DRGCoder’s reasoning by visualizing the weights for each word of the input. Additionally, DRGCoder allows users to identify diseases within discharge summaries and compare across multiple discharge summaries. Our demo is available at https://huggingface.co/spaces/danielhajialigol/DRGCoder. A video demonstrating the demo can be found at https://www.youtube.com/watch?v=pcdiG6VwqlA
%R 10.18653/v1/2023.emnlp-demo.34
%U https://aclanthology.org/2023.emnlp-demo.34
%U https://doi.org/10.18653/v1/2023.emnlp-demo.34
%P 373-380

Markdown (Informal)

[DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups](https://aclanthology.org/2023.emnlp-demo.34) (Hajialigol et al., EMNLP 2023)

• DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups (Hajialigol et al., EMNLP 2023)

ACL

• Daniel Hajialigol, Derek Kaknes, Tanner Barbour, Daphne Yao, Chris North, Jimeng Sun, David Liem, and Xuan Wang. 2023. DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 373–380, Singapore. Association for Computational Linguistics.