Improving Machine Understanding of Czech Medical Text Using Self-Supervised and Rule-Based Data Augmentation
| Autoři | |
|---|---|
| Rok publikování | 2025 |
| Druh | Článek ve sborníku |
| Konference | Modeling Decisions for Artificial Intelligence, 22nd International Conference, MDAI 2025 |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | https://link.springer.com/chapter/10.1007/978-3-032-00891-6_25 |
| Doi | http://dx.doi.org/10.1007/978-3-032-00891-6_25 |
| Klíčová slova | EHR; health records; medical text; clinical text; data augmentation; annotation; self-supervised; bootstrapping; Czech |
| Popis | Medical doctor decision-making benefits from the development of effective support software. But for software to accurately interpret meaning and assist in clinical contexts, high-quality annotated health record data must be available for training and evaluation. This paper addresses this issue in the Czech language context, detailing a stage in a unique electronic health record (EHR) bootstrapping project. Using over 42 million words of Czech oncology records, we curated the creation of the CSEHR dataset: over 62,000 words of text with manually annotated medical concepts, out of which over 12,000 have been developed through multiple stages of review to serve as ground truth. We are leveraging this seed data to bootstrap larger annotated corpora, enabling scalable development of Czech healthcare NLP applications. This paper focuses on combining two data augmentation approaches. Approach 1, semi-supervised, consists in automated dataset augmentation using self-annotation to increase annotation density. Approach 2, based on distant supervision, consists in manual development of rules for improving annotations in training data. Results show that combining these two approaches on training data and fine-tuning an XLM-RoBERTa model for entity recognition increases the token classification F1 score by more than 5 points. This demonstrates the promise of this technique in further bootstrapping steps. |
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