title: RuCCoD: Towards Automated ICD Coding in Russian
summary: This study investigates the feasibility of automating clinical coding in
  Russian, a language with limited biomedical resources. We present a new dataset
  for ICD coding, which includes diagnosis fields from electronic health records
  (EHRs) annotated with over 10,000 entities and more than 1,500 unique ICD
  codes. This dataset serves as a benchmark for several state-of-the-art models,
  including BERT, LLaMA with LoRA, and RAG, with additional experiments examining
  transfer learning across domains (from PubMed abstracts to medical diagnosis)
  and terminologies (from UMLS concepts to ICD codes). We then apply the
  best-performing model to label an in-house EHR dataset containing patient
  histories from 2017 to 2021. Our experiments, conducted on a carefully curated
  test set, demonstrate that training with the automated predicted codes leads to
  a significant improvement in accuracy compared to manually annotated data from
  physicians. We believe our findings offer valuable insights into the potential
  for automating clinical coding in resource-limited languages like Russian,
  which could enhance clinical efficiency and data accuracy in these contexts.
paper_authors._id: 67ced7b271b2e0c1f985fb3a
paper_authors.name: Aleksandr Nesterov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T17:39:05.726Z
paper_authors._id: 67471a8767d4205e5c6a2026
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/qCPd8Das5IZ4GugFxpIIU.png
paper_authors.fullname: Alexandr Nesterov
paper_authors.type: user
paper_authors.user: newneonestor
paper_authors._id: 67ced7b271b2e0c1f985fb3b
paper_authors.name: Andrey Sakhovskiy
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T17:39:07.898Z
paper_authors._id: 6504b078074aa8f31081f528
paper_authors.avatarUrl: /avatars/63b88efd9a51c4912b98da0ea874e63b.svg
paper_authors.fullname: Andrei Sakhovskii
paper_authors.type: user
paper_authors.user: andorei
paper_authors._id: 67ced7b271b2e0c1f985fb3c
paper_authors.name: Ivan Sviridov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T12:46:22.500Z
paper_authors._id: 61dedb1b2066746d68b63adb
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61dedb1b2066746d68b63adb/PLBHQxvbcay3qDJjY7HM3.jpeg
paper_authors.fullname: Ivan Sviridov
paper_authors.type: user
paper_authors.user: univanxx
paper_authors._id: 67ced7b271b2e0c1f985fb3d
paper_authors.name: Airat Valiev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:59:04.593Z
paper_authors._id: 64e5b6b857d6d38e126ca802
paper_authors.avatarUrl: /avatars/8b2e7823f8c34581ac9cc52984b0504b.svg
paper_authors.fullname: Airat Valiev
paper_authors.type: user
paper_authors.user: avaliev
paper_authors._id: 67ced7b271b2e0c1f985fb3e
paper_authors.name: Vladimir Makharev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T12:46:24.513Z
paper_authors._id: 6563a8dd8fb38d71f77ee20a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/WthtysRoxzNvT5AlIwPlR.jpeg
paper_authors.fullname: Vladimir Makharev
paper_authors.type: user
paper_authors.user: sm1rk
paper_authors._id: 67ced7b271b2e0c1f985fb3f
paper_authors.name: Petr Anokhin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T17:39:10.948Z
paper_authors._id: 64f734b08e7fa6529fb693f1
paper_authors.avatarUrl: /avatars/a50c7e1d22174f06fbaf99e8caca7c37.svg
paper_authors.fullname: Petr Anokhin
paper_authors.type: user
paper_authors.user: petranokhin
paper_authors._id: 67ced7b271b2e0c1f985fb40
paper_authors.name: Galina Zubkova
paper_authors._id: 67ced7b271b2e0c1f985fb41
paper_authors.name: Elena Tutubalina
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T13:35:18.779Z
paper_authors._id: 662f8d645c4db70c77a203b0
paper_authors.avatarUrl: /avatars/72f9a3c39b3ba5114388d16a35524835.svg
paper_authors.fullname: Elena Tutubalina
paper_authors.type: user
paper_authors.user: tlenusik
paper_title: RuCCoD: Towards Automated ICD Coding in Russian
paper_summary: This study investigates the feasibility of automating clinical coding in
  Russian, a language with limited biomedical resources. We present a new dataset
  for ICD coding, which includes diagnosis fields from electronic health records
  (EHRs) annotated with over 10,000 entities and more than 1,500 unique ICD
  codes. This dataset serves as a benchmark for several state-of-the-art models,
  including BERT, LLaMA with LoRA, and RAG, with additional experiments examining
  transfer learning across domains (from PubMed abstracts to medical diagnosis)
  and terminologies (from UMLS concepts to ICD codes). We then apply the
  best-performing model to label an in-house EHR dataset containing patient
  histories from 2017 to 2021. Our experiments, conducted on a carefully curated
  test set, demonstrate that training with the automated predicted codes leads to
  a significant improvement in accuracy compared to manually annotated data from
  physicians. We believe our findings offer valuable insights into the potential
  for automating clinical coding in resource-limited languages like Russian,
  which could enhance clinical efficiency and data accuracy in these contexts.
paper_ai_summary: Experiments on a new Russian-language ICD coding dataset using models like BERT, LLaMA with LoRA, and RAG show significant accuracy improvements in automated clinical coding compared to manual annotations.
paper_ai_keywords: BERT LLaMA LoRA RAG transfer learning EHR ICD coding clinical coding UMLS concepts automated predicted codes