Alexandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov, Airat Valiev, Vladimir Makharev, Petr Anokhin, Galina Zubkova, Elena Tutubalina
| Challenge: | a new dataset for clinical coding in Russian is available for download . human coders must navigate a wide array of medical terminology and time pressures . |
| Approach: | They present a new dataset for ICD coding in Russian, a language with limited biomedical resources. |
| Outcome: | The proposed model improves accuracy on an in-house EHR dataset from 2017 to 2021. |
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Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, Sergey Nikolenko
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Applications of BERT Models Towards Automation of Clinical Coding in Icelandic (2024.findings-naacl)
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| Challenge: | Traditionally, clinical coding is manual and laborintensive task prone to human error. |
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| Challenge: | Existing automated ICD coding systems face several fundamental challenges due to the limited availability of publicly available Chinese ICD datasets. |
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| Challenge: | Recent work on automated ICD coding learn mappings between low-dimensional representations of clinical text reports and codes. |
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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. |
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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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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. |
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