RuCCoD: Towards Automated ICD Coding in Russian
Authors: name: Aleksandr Nesterov, name: Andrey Sakhovskiy, name: Ivan Sviridov, name: Airat Valiev, name: Vladimir Makharev, name: Petr Anokhin, name: Galina Zubkova, name: Elena Tutubalina

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.