| 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. |
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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. |
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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Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review (2025.acl-long)
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| Challenge: | Clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. |
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Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine (D19-62)
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| Challenge: | Existing work has trained medical embeddings to rep-resent medical concepts using specific medical data. |
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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. |
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Distributed Knowledge Based Clinical Auto-Coding System (P19-2)
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| 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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Modelling Temporal Document Sequences for Clinical ICD Coding (2023.eacl-main)
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| Challenge: | Existing studies on the ICD coding task focus on extracting codes from the discharge summary, but there is potential to automate the task by identifying relevant information from clinical notes. |
| Approach: | They propose a hierarchical transformer architecture that uses text across the entire sequence of clinical notes in each hospital stay for ICD coding. |
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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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Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition (P19-2)
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| Challenge: | Off-the-shelf word embeddings tend to perform poorly on texts from specialized domains such as clinical reports. |
| Approach: | They combine off-the-shelf contextual embeddings with static word2vec embedders trained on a small in-domain corpus built from task data to reach and sometimes outperform representations learned from a large corpus in the medical domain. |
| Outcome: | The proposed embedding strategies outperform representations learned from a large corpus in the medical domain. |
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. |
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