Ukrainian Cardiology NER (uk-cardio-ner)

Named Entity Recognition (NER) model for Ukrainian medical discharge summaries Trained on real cardiology cases from the cardiovascular surgery clinic. The dataset used for training underwent rigorous cleaning, thorough depersonalization, and complete anonymization to ensure strict compliance with medical data privacy standards.

Model Description

This model extracts medication-related entities from Ukrainian clinical texts. It was trained using spaCy with the youscan/ukr-roberta-base transformer as the backbone.

Entities

Label	Description	Example
DRUG	Drug name (trade or INN)	Бісопролол, Ксарелто
DOSE	Dosage	5 мг, 10/2,5/5 мг
FREQ	Frequency	щоранку, 2 р/д
DUR	Duration	тривало, 14 діб

Training Data

• Source: Discharge summaries from cardiovascular surgery clinic
• Size: 5,855 annotated medication prescription strings
• Language: Ukrainian
• Domain: Cardiology, cardiac surgery
• Annotation: Auto-labeled via DeepSeek LLM + manual validation

Performance

Evaluated on 1,171 held-out examples (20% split):

Metric	Score
F1	90.21
Precision	91.02
Recall	89.42

Usage

import spacy
nlp = spacy.load("uk-cardio-ner")
doc = nlp("Бісопролол 5мг щоранку, тривало.")
for ent in doc.ents:
    print(ent.text, ent.label_)
# Бісопролол DRUG
# 5мг DOSE
# щоранку FREQ
# тривало DUR

Citation

If you use this model, please cite:

TAIS DSS — Trusted AI for Intelligent Specialists IntracomUA OÜ, 2026 https://tais.esemi.org