README.md

metadata

license: cc-by-nc-sa-4.0
language:
  - ru
multilinguality:
  - monolingual
annotations_creators:
  - expert-generated
source_datasets:
  - original
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: RuClinNER
size_categories:
  - 1K<n<10K
tags:
  - clinical
  - medical
  - biomedical
  - named-entity-recognition
  - nested-ner
  - relation-extraction
  - entity-linking
  - icd-10
  - russian
configs:
  - config_name: medterm
    data_files:
      - split: train
        path: data/medterm/train.parquet
      - split: test
        path: data/medterm/test.parquet
  - config_name: drugrel
    data_files:
      - split: train
        path: data/drugrel/train.parquet
      - split: test
        path: data/drugrel/test.parquet
  - config_name: covid_rmj
    data_files:
      - split: train
        path: data/covid_rmj/train.parquet
      - split: test
        path: data/covid_rmj/test.parquet

RuClinNER

RuClinNER is a collection of three complementary Russian clinical named-entity-recognition corpora. They span distinct kinds of clinical text written in healthcare settings — patient-reported complaints, physician prescriptions, and peer-reviewed clinical research articles — and together contain 55,185 entity spans across 22 entity types and 18,112 relations across 8 relation types over 6,797 records.

The collection accompanies the paper RuClinNER: Russian Clinical Multi-Label NER Annotated Corpora. It is intended for research on Russian clinical and biomedical NLP, where annotated clinical text remains scarce: existing Russian resources largely target consumer drug reviews or scientific abstracts rather than text produced in clinical practice.

The three corpora

Config	Text type	Records	Entities	Types	Relations	Median words	Notable
medterm	patient complaints	4,474	12,582	3	—	9	ICD-10 normalization subset
drugrel	drug prescriptions	2,200	35,635	13	18,112	28	nested entities + relations; patient metadata
covid_rmj	clinical journal articles	123	6,968	6	—	2,386	long documents; dual annotation layers
Total		6,797	55,185	22	18,112

• MedTerm — short, free-text symptom narratives from a symptom-checking service. Three entity types (SYMPTOM, RISK, DISEASE). A curated subset of entities is linked to ICD-10 canonical concepts (Russian and English names), supporting clinical concept normalization / entity linking.
• DrugRel — Russian clinical prescription records. Thirteen entity types covering drug names, dosage components, and modifiers, with hierarchically nested entities: a REGIME span is a container whose tokens also carry nested labels (DOSE, DOSIR, FREQ, DUR, TIME, USE, …). Eight relation types link attributes to their drug. Records are linked to de-identified patient metadata (age, gender, ICD-10 diagnosis codes).
• COVID-RMJ — full-length COVID-19 articles from the Russian Medical Journal, annotated by medical experts over six entity types. Two parallel annotation layers are provided: the full article text and a structured abstract, each with character spans and pre-computed BIO tags.

What the data looks like

MedTerm — patient complaint

A single short complaint often mixes all three entity types:

зевота, нехватка кислорода, астма, аллергия

span	label
зевота	SYMPTOM
нехватка кислорода	SYMPTOM
астма	DISEASE
аллергия	RISK

ICD-10 normalization. A curated MedTerm subset links spans to canonical concepts. Patients write the same concept many ways — Fever (R50.9) alone appears as, among 50+ forms:

температура · Температура 38 · t 37 · 37,4 · до 39 · лихорадка · температкра · Темперара

(the last two are genuine patient typos) — all normalized to Fever, unspecified — R50.9.

DrugRel — prescription with nested entities and relations

A REGIME is a container span: the tokens inside it also carry nested labels, and each attribute is linked back to its drug by a typed relation.

Левофлоксацин по 500 мг 1 раз в день 5 дней

• DRUG — Левофлоксацин
• REGIME — по 500 мг 1 раз в день 5 дней (container — its tokens carry a second label)
  • DOSE — по 500 мг
  • FREQ — 1 раз в день
  • DUR — 5 дней
• Relations: DOSE → DRUG, REGIME → DRUG

COVID-RMJ — clinical journal article

Dense, expert-annotated entities over long documents:

после положительного результата ПЦР на SARS-CoV-2 различные симптомы (усталость — 39,0 %, головная боль — 23,2 %, одышка — 23,4 % и др.)

span	label
ПЦР	PROCEDURE
SARS-CoV-2	DISEASE
усталость · головная боль · одышка	SYMPTOM

Supported tasks

The corpora support several clinical-NLP tasks:

• Named entity recognition — all three corpora (character-offset spans).
• Nested / multi-label token classification — DrugRel REGIME containers (≈22,900 overlapping span pairs); a single token may carry more than one label.
• Relation extraction — DrugRel drug–attribute relations (8 types).
• Clinical concept normalization / entity linking — MedTerm entities linked to ICD-10.
• Long-document NER — COVID-RMJ articles (median ≈2,400 words).
• Cross-corpus and domain-transfer studies, and evaluation of LLM prompting for clinical information extraction across complementary text types.

Usage

from datasets import load_dataset

medterm = load_dataset("medlinx/RuClinNER", "medterm")
drugrel = load_dataset("medlinx/RuClinNER", "drugrel")
covid   = load_dataset("medlinx/RuClinNER", "covid_rmj")

ex = medterm["test"][0]
print(ex["text"])
for e in ex["entities"]:
    print(e["label"], ":", e["text"], e.get("coding"))

Data fields

medterm

• id (string), text (string)
• entities: list of {start, end, label, text, coding} where coding is {icd10, name_ru, name_en} for the normalized subset, else null.

drugrel

• id, record_id (string), text (string)
• entities: list of {id, start, end, label, text}
• relations: list of {type, from_id, to_id, from_text, to_text}
• patient_age (int, nullable), patient_gender (string, nullable)
• diagnosis (string), diagnosis_icd_codes (list of string)

covid_rmj

• id, text (string)
• text_entities: list of {start, end, label, text} over text
• text_tokens, text_ner_tags: pre-tokenized text with BIO tags
• annotation (string, structured abstract) with annotation_entities, annotation_tokens, annotation_ner_tags

Splits

Config	train	test
medterm	3,049	1,425
drugrel	2,100	100
covid_rmj	98	25

For MedTerm, the ICD-10 normalization subset (508 records, 1,294 linked entities) falls within the test split. For COVID-RMJ, a train/test split is provided here; because the corpus is small (123 documents), the accompanying paper instead evaluates encoders with 5-fold cross-validation.

Entity and relation types

MedTerm — SYMPTOM (symptoms/signs), RISK (risk factors), DISEASE (named conditions).

DrugRel — DRUG, REGIME (full dosage regimen; container), DOSE (numeric dose), DOSIR (per-intake instruction), FREQ, DUR, TIME, USE (route), FORM, COND (condition for taking), INN (active substance), NUM, NEG. Relations: REGIME-DRUG, FORM-DRUG, DOSE-DRUG, USE-DRUG, COND-DRUG, INN-DRUG, NEG-DRUG, ALT.

COVID-RMJ — DISEASE, PROCEDURE, SYMPTOM, DRUG, ANATOMY, LAB_VALUE.

Dataset creation

Each corpus was annotated by domain experts (medical informaticians, clinical pharmacologists, and medical specialists) following written guidelines. Records were independently annotated and disagreements adjudicated. Inter-annotator agreement (Cohen's κ): MedTerm 0.87 (boundaries) / 0.91 (labels); DrugRel 0.89 (entities) / 0.81 (relations). COVID-RMJ was reviewed by at least two experts with adjudication and passes automated boundary/overlap/BIO consistency checks.

Considerations for using the data

• Class imbalance is substantial and corpus-specific (e.g. SYMPTOM ≈85% of MedTerm spans; DISEASE ≈61% of COVID-RMJ spans). Report macro- as well as micro-averaged metrics.
• MedTerm contains genuine negatives: ≈18% of records have no annotated entity.
• DrugRel includes 67 relations with type=null (annotation artifacts); exclude these for relation-extraction evaluation. The RISK/DISEASE distinction can be semantically ambiguous (e.g. obesity).
• DrugRel demographics skew toward female patients (≈74%), with many pediatric and obstetric records; consider this when transferring models to other populations.
• COVID-RMJ entity density varies ~100× across documents.

Personal and sensitive information

The data is de-identified: MedTerm and DrugRel records contain no patient names, dates of birth, or contact information; COVID-RMJ articles are published, peer-reviewed texts with no patient-level data. DrugRel patient metadata is limited to coarse age, gender, and ICD-10 diagnosis codes.

Licensing

Released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0) for research use.

Citation

@misc{ruclinner2026,
  title        = {RuClinNER: Russian Clinical Multi-Label NER Annotated Corpora},
  author       = {Gudkov, Vadim and Varennikova, Anastasia and Miroshnichenko, Polina and
                  Mitrofanova, Olga and Gousyatskaya, Polina and Boitsova, Daria},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/medlinx/RuClinNER}}
}

Contact

Maintained by Medlinx. Questions: gudkov.v@medlinx.online.