DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups

Challenge:	Medical claim coding is the process of transforming medical records into structured codes in a classification system such as ICD-10 (International Classification of Diseases, Tenth Revision) or DRG (Diagnosis-Related Group) codes.
Approach:	They propose an explainability-enhanced clinical claim coding system for the early prediction of medical severity DRGs (MS-DRGs) a novel multi-task Transformer model allows users to inspect DRGCoder’s reasoning by visualizing the weights for each word of the input.
Outcome:	The proposed system allows users to analyze the weights of the input and compare across multiple discharge summaries.

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Overview

Challenge

Approach

Outcome

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James C. Douglas, Yidong Gan, Ben Hachey, Jonathan K. Kummerfeld

Responsible & Trustworthy NLP

Challenge:	Clinical coding is labor-intensive and prone to delays, leading to global backlogs.
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CPTCoder: A Reliable LLM System for Medical Procedure Code Prediction (2026.acl-demo)

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Benlu Wang, Ziyao Shangguan, Kyle Tegtmeyer, Zhenyu Zhang, Sophie Chheang, Arman Cohan

Challenge:	Clinical procedure coding is an extreme multi-label classification problem . CPTCoder predicts standardized medical procedure codes from clinical text .
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Analyzing Code Embeddings for Coding Clinical Narratives (2021.findings-acl)

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Wei Shi, Jiewen Wu, Xiwen Yang, Nancy Chen, Ivan Ho Mien, Jung-Jae Kim, Pavitra Krishnaswamy

Representation Learning

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MedCodER: A Generative AI Assistant for Medical Coding (2025.naacl-industry)

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Krishanu Das Baksi, Elijah Soba, John J Higgins, Ravi Saini, Jaden Wood, Jane Cook, Jack I Scott, Nirmala Pudota, Tim Weninger, Edward Bowen, Sanmitra Bhattacharya

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MedCPI: A Construct–Personalize–Integrate Framework for KG-enhanced Clinical Prediction (2026.findings-acl)

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Hang Wang, Hang Dong, Lu Liu

Knowledge Representation

Challenge:	Existing KG-enhanced approaches to clinical prediction are limited . existing approaches to personalize and integrate knowledge are weakly controlled .
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Distributed Knowledge Based Clinical Auto-Coding System (P19-2)

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Rajvir Kaur

Information Retrieval

Challenge:	Codification of free-text clinical narratives has long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research.
Approach:	They propose to use NLP and related machine learning techniques to assign ICD-10-AM and ACHI codes to clinical records using local and international standards.
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A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients (2020.lrec-1)

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Edward T. Moseley, Joy T. Wu, Jonathan Welt, John Foote, Patrick D. Tyler, David W. Grant, Eric T. Carlson, Sebastian Gehrmann, Franck Dernoncourt, Leo Anthony Celi

Challenge:	Currently, most medical data is generated and stored in unstructured, text-based format.
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MedicalSum: A Guided Clinical Abstractive Summarization Model for Generating Medical Reports from Patient-Doctor Conversations (2022.findings-emnlp)

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George Michalopoulos, Kyle Williams, Gagandeep Singh, Thomas Lin

Text Generation

Challenge:	Existing models for summarizing medical conversations do not take clinical knowledge into account and are difficult to control.
Approach:	They propose a transformer-based sequence-to-sequence architecture for summarizing medical conversations by integrating medical domain knowledge from the Unified Medical Language System (UMLS).
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Automatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings (2022.coling-1)

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Shurui Zhang, Bozheng Zhang, Fuxin Zhang, Bo Sang, Wanchun Yang

Semantic Text Processing

Challenge:	Existing studies did not exploit the discourse structure of clinical notes, which provides rich contextual information for code assignment.
Approach:	They propose to leverage section type classification and section type embeddings to exploit the discourse structure of clinical notes to generate rich contextual information for code assignment.
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Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation (2025.emnlp-industry)

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Moy Yuan, Han-Chin Shing, Mitch Strong, Chaitanya Shivade

Language Models

Challenge:	Existing methods for clinical code verification fail to account for hierarchical misalignments . standardized coding systems such as ICD-10-CM1 ensure consistency across medical records.
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