Extending CARDIO:DE: Additional annotation guidelines and evaluation of NLP approaches for clinical applications
The extended CARDIO:DE dataset, with its refined annotation guidelines provides a robust foundation for natural language processing applications in the clinical domain. The performance of the TinyLlama model demonstrates the potential of fine-tuning non-domain-specific models for clinical text processing. This work paves the way for more accurate NLP solutions in healthcare, particularly for information extraction and decision support in cardiology.
Authors
- Matthias Becker (ORCID: https://orcid.org/0000-0003-4358-1258)
- Alexander Karl (ORCID: https://orcid.org/0000-0003-2221-8038)
- Phillip Richter-Pechanski (ORCID: https://orcid.org/0000-0003-0121-373X)
- Mario Krumscheid
- Alisa Knobelspies
- Markus Seydel
Institutions
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Kempten University of Applied Sciences (DE)
- Einstein Center Digital Future (DE)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1016/j.ijmedinf.2025.106009
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00