Papers with International Classification of Diseases
AnEMIC: A Framework for Benchmarking ICD Coding Models (2022.emnlp-demos)
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| Challenge: | Diagnostic coding is the task of assigning diagnosis codes defined by the ICD (International Classification of Diseases) standard to patient visits based on clinical notes. |
| Approach: | They propose to use an ICD coding framework to train and benchmark models . they correct errors in preprocessing and provide an interactive demo to analyze the models based on custom inputs. |
| Outcome: | The framework corrects errors in preprocessing and provides key models and weights trained on correctly preprocessed datasets. |
Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment (2023.acl-srw)
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| Challenge: | The Ninth Revision of the International Classification of Diseases (ICD-9) is a standardized coding system used worldwide to classify and code diseases, injuries, and other health conditions. |
| Approach: | They evaluate the usefulness of correlation bias and suggest it could improve ICD-9 code assignment in some cases. |
| Outcome: | The proposed model improves on classes that are more imbalanced and less correlated with other codes, but the effect on individual class can be negative or positive. |
DRGCoder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups (2023.emnlp-demo)
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Daniel Hajialigol, Derek Kaknes, Tanner Barbour, Daphne Yao, Chris North, Jimeng Sun, David Liem, Xuan Wang
| 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. |
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
| Challenge: | Medical coding is time-consuming and error-prone due to large label space, lengthy text inputs, and the absence of supporting evidence annotations. |
| Approach: | They propose a Generative AI framework for automatic medical coding that leverages extraction, retrieval, and re-ranking techniques as core components. |
| Outcome: | The proposed framework outperforms existing methods on the International Classification of Diseases (ICD) code prediction scale. |
A Neural Architecture for Automated ICD Coding (P18-1)
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| Challenge: | Medical coding is time-consuming, expensive, and error prone. |
| Approach: | They propose to use diagnosis descriptions (DDs) of a patient as inputs to select the most relevant ICD codes. |
| Outcome: | The proposed algorithms perform on a clinical dataset with 59K patient visits. |
Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings (2024.eacl-long)
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| Challenge: | Existing approaches to assigning ICD codes to clinical text are time-consuming, labor intensive, and error-prone. |
| Approach: | They propose to adapt a Transformer-based model to a longformer model and use it to encode clinical narratives. |
| Outcome: | The proposed approach outperforms current state-of-the-art models in ICD coding with the label embeddings contributing to the good performance. |
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification (2024.lrec-main)
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| Challenge: | Existing methods for ICD indexing use machine learning to assign subset of codes to medical records . experimental results show proposed method achieves state-of-the-art performance on a number of measures. |
| Approach: | They propose a method that uses a deep dilated residual convolution encoder to learn document representations across different lengths of the texts. |
| Outcome: | The proposed method achieves state-of-the-art performance on a number of measures. |
Fusion: Towards Automated ICD Coding via Feature Compression (2021.findings-acl)
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| Challenge: | Existing methods to assign ICD codes from unstructured clinical notes are noisy and prone to errors. |
| Approach: | They propose a feature compressed ICD coding model called Fusion to address this problem. |
| Outcome: | The proposed model outperforms existing models on two widely used datasets. |
Travel on the ICD Tree: Benchmarking Agentic Reasoning for ICD Coding from Chinese Electronic Medical Records (2026.findings-acl)
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| Challenge: | Accurate International Classification of Diseases (ICD) coding is crucial for hospital management and healthcare data governance. |
| Approach: | They propose a framework to evaluate ICD coding based on complete EMRs . they use a dataset of 560 real clinical records covering 434 common diseases . |
| Outcome: | The proposed framework explores the capability boundaries of large language models under different paradigms. |
Evaluation and LLM-Guided Learning of ICD Coding Rationales (2026.eacl-long)
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| Challenge: | Existing studies on the explainability of ICD coding rely on attention-based rationales and qualitative assessments conducted by physicians. |
| Approach: | They propose to evaluate the explainability of rationales in ICD coding using a multi-granular rationale-annotated dataset. |
| Outcome: | The proposed model improves the explainability of rationales in ICD coding by using human-annotated rationale-announced rationale models. |
Automatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings (2022.coling-1)
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| 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. |
| Outcome: | The proposed model outperforms state-of-the-art models on a MIMIC dataset by a large margin. |
Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation (2024.naacl-long)
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| Challenge: | Existing methods for ICD indexing have a heavy label distribution and a manual process . Xie and Xing (2017) propose a new approach to ICD re-ranking . |
| Approach: | They propose a "retrieve and re-rank" framework to allocate subsets of ICD codes to medical records . they leverage auxiliary knowledge of the electronic health records (EHR) and a discrete retrieval method . |
| Outcome: | The proposed method achieves state-of-the-art performance on the MIMIC-III benchmark. |
HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding (2020.acl-main)
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| Challenge: | Existing methods for ICD coding ignore Code Hierarchy and Code Co-occurrence . cost of manual coding estimated to be $25 billion per year in the US . |
| Approach: | They propose a hyperbolic representation method to leverage the code hierarchy and a graph convolutional network to utilize the code co-occurrence. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two widely used datasets. |
A Two-Stage Decoder for Efficient ICD Coding (2023.findings-acl)
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| Challenge: | Recent automated ICD coding efforts improve performance by encoding medical notes and codes with additional data and knowledge bases. |
| Approach: | They propose a two-stage decoding mechanism to predict ICD codes using hierarchical properties of the codes to split the prediction into two steps: at first, predict the parent code and then predict the child code based on the previous prediction. |
| Outcome: | Experiments on the public MIMIC-III data show that the proposed model performs well in single-model settings without external data or knowledge. |
Data Drift in Clinical Outcome Prediction from Admission Notes (2024.lrec-main)
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Paul Grundmann, Jens-Michalis Papaioannou, Tom Oberhauser, Thomas Steffek, Amy Siu, Wolfgang Nejdl, Alexander Loeser
| Challenge: | a pivotal dataset for clinical NLP research was released in 2016 . public access to such datasets is limited due to privacy and ethical concerns . |
| Approach: | They propose a novel clinical outcome prediction dataset based on MIMIC-IV . they provide initial insights into the performance of models trained on MIDIC-III . |
| Outcome: | The proposed dataset aims to probe the robustness and generalization of clinical outcome prediction models . the study focuses on challenges tied to evolving documentation standards and changing codes in the ICD taxonomy . |
Analyzing Code Embeddings for Coding Clinical Narratives (2021.findings-acl)
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| Challenge: | Recent work on automated ICD coding learn mappings between low-dimensional representations of clinical text reports and codes. |
| Approach: | They propose novel neural networks for encoding medical codes based on textual, structural and statistical characteristics using a single deep learning baseline model. |
| Outcome: | The proposed methods improve the accuracy of medical codes based on their textual, structural and statistical characteristics. |
Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes (D19-1)
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| Challenge: | a neural architecture learns to generate content in a specific order without explicit specifications of the relations between input entities and output entities. |
| Approach: | They propose a natural language generation task that generates discharge instructions from ICD codes . they propose to model content ordering and text generation in a specific order . |
| Outcome: | The proposed model outperforms baseline models in BLEU scores and human evaluation. |
ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding (2026.acl-long)
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| Challenge: | Existing models lack convincing, human-understandable explanations, making them difficult for physicians to trust and use in practice. |
| Approach: | They propose a framework that aims to automatically assign ICD codes to clinical notes while providing explicit justifications for each assignment. |
| Outcome: | The proposed framework achieves effective ICD coding with accurate explanations using two collaborative LLM agents: a coding agent and a critical agent. |