Extending CARDIO:DE: additional annotation guidelines and evaluation of NLP approaches for clinical applications - Becker, Matthias; Krumscheid, Mario; Knobelspies, Alisa; Seydel, Markus; Richter-Pechanski, Phillip; Karl, Alexander (International journal of medical informatics, 2025)
Background - Cardiovascular diseases are a major cause of morbidity and mortality, and the management of these conditions generates extensive clinical data. The CARDIO:DE dataset, a German-language corpus of cardiovascular clinical routine letters, has been developed to support natural language processing research. This study seeks to enhance the dataset by introducing refined annotation guidelines and expanding the annotation schema. - Objective - The objective of this study was to extend the CARDIO:DE dataset with additional annotation categories, and evaluate state-of-the-art NLP models to enhance the utility of the dataset for clinical applications. - Methods - The annotation schema was expanded to include categories such as diagnostic procedures, medical finding, and therapeutic interventions (Diagnostic, Diagnosis, Drug, Medical_Finding, Therapy). The iterative annotation process involved expert annotators, ensuring high-quality, consistent annotations. Four models—GBERT, medBERT.de, XLM-RoBERTa, and TinyLlama—were fine-tuned and evaluated on the dataset. Model performance was assessed using entity-wise precision, recall, and F1 scores. - Results - The extended dataset includes 304,582 token-based annotations, with the highest concentration in medical finding. The inter-annotator agreement scores improved during the iterative process, reaching up to 0.98 for certain subsets. Among the evaluated models, TinyLlama outperformed the other models in entity recognition, achieving a macro-average F1 score of 0.845, highlighting its potential for clinical NLP tasks. - Conclusions - 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.