| 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. |
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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. |
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. |
A General Knowledge Injection Framework for ICD Coding (2025.findings-acl)
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| Challenge: | Existing methods to improve ICD coding focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other. |
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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
| Challenge: | Medical coding is time-consuming and error-prone due to large label space, lengthy text inputs, and the absence of supporting evidence annotations. |
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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. |
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CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning (2024.lrec-main)
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| Challenge: | Existing methods for boosting ICD coding performance lack a model for complex code relations . current methods overlook the importance of context in clinical notes . |
| Approach: | They propose a contextualized and flexible framework to enhance learning of ICD code relations . they use clinical notes to model all possible code relations using a dependent learning paradigm . |
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Code Like Humans: A Multi-Agent Solution for Medical Coding (2025.findings-emnlp)
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Andreas Geert Motzfeldt, Joakim Edin, Casper L. Christensen, Christian Hardmeier, Lars Maaløe, Anna Rogers
| Challenge: | In medical coding, experts map unstructured clinical notes to alphanumeric codes for diagnoses and procedures. |
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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. |
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Less is More: Explainable and Efficient ICD Code Prediction with Clinical Entities (2025.acl-long)
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| Challenge: | Clinical coding is labor-intensive and prone to delays, leading to global backlogs. |
| Approach: | They propose an approach that combines Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. |
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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. |
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