RuCCoD: Towards Automated ICD Coding in Russian
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.
67ced7b271b2e0c1f985fb3a
Aleksandr Nesterov
claimed_verified
2025-03-10T17:39:05.726Z
67471a8767d4205e5c6a2026
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/qCPd8Das5IZ4GugFxpIIU.png
Alexandr Nesterov
user
newneonestor
67ced7b271b2e0c1f985fb3b
Andrey Sakhovskiy
claimed_verified
2025-03-10T17:39:07.898Z
6504b078074aa8f31081f528
/avatars/63b88efd9a51c4912b98da0ea874e63b.svg
Andrei Sakhovskii
user
andorei
67ced7b271b2e0c1f985fb3c
Ivan Sviridov
claimed_verified
2025-03-10T12:46:22.500Z
61dedb1b2066746d68b63adb
https://cdn-avatars.huggingface.co/v1/production/uploads/61dedb1b2066746d68b63adb/PLBHQxvbcay3qDJjY7HM3.jpeg
Ivan Sviridov
user
univanxx
67ced7b271b2e0c1f985fb3d
Airat Valiev
claimed_verified
2025-03-14T08:59:04.593Z
64e5b6b857d6d38e126ca802
/avatars/8b2e7823f8c34581ac9cc52984b0504b.svg
Airat Valiev
user
avaliev
67ced7b271b2e0c1f985fb3e
Vladimir Makharev
claimed_verified
2025-03-10T12:46:24.513Z
6563a8dd8fb38d71f77ee20a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/WthtysRoxzNvT5AlIwPlR.jpeg
Vladimir Makharev
user
sm1rk
67ced7b271b2e0c1f985fb3f
Petr Anokhin
claimed_verified
2025-03-10T17:39:10.948Z
64f734b08e7fa6529fb693f1
/avatars/a50c7e1d22174f06fbaf99e8caca7c37.svg
Petr Anokhin
user
petranokhin
67ced7b271b2e0c1f985fb40
Galina Zubkova
67ced7b271b2e0c1f985fb41
Elena Tutubalina
claimed_verified
2025-03-10T13:35:18.779Z
662f8d645c4db70c77a203b0
/avatars/72f9a3c39b3ba5114388d16a35524835.svg
Elena Tutubalina
user
tlenusik
RuCCoD: Towards Automated ICD Coding in Russian
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.
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.
BERT LLaMA LoRA RAG transfer learning EHR ICD coding clinical coding UMLS concepts automated predicted codes