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.

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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
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article

Extending CARDIO:DE: Additional annotation guidelines and evaluation of NLP approaches for clinical applications

Matthias Becker, Alexander Karl, Phillip Richter-Pechanski, Mario Krumscheid et al.
International Journal of Medical Informatics
Topic Modeling
article

Extending CARDIO:DE: Additional annotation guidelines and evaluation of NLP approaches for clinical applications

Matthias Becker, Alexander Karl, Phillip Richter-Pechanski, Mario Krumscheid, Alisa Knobelspies, Markus Seydel
article en

Abstract

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.

International Journal of Medical InformaticsVol. 203
Heidelberg University (DE), University Hospital Heidelberg (DE), Kempten University of Applied Sciences (DE), Einstein Center Digital Future (DE)
Openalex Percentile: Top 94%
Topic Modeling
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