Purpose Natural Language Processing (NLP) has the potential to extract structured clinical knowledge from unstructured Electronic Health Records (EHRs). However, the limited availability of annotated datasets for algorithm training restricts its application in clinical practice. This study investigates the use of transformer-based NLP models to structure Italian EHRs in cardiac settings, addressing this gap. Methods We implemented and evaluated three named entity recognition algorithms: SpaCy, Flair, and Multiconer. The experiments utilized three datasets comprising 2235 anamneses from patients at the Fondazione Toscana Gabriele Monasterio, Italy. Results The SpaCy model achieved the highest performance with an F1-score of 97% in identifying clinical features on explicitly mentioned entities (Presence/Absence classification). However, features are not always mentioned, as clinicians selectively document only clinically relevant information in real-world practice. External validation shows model generalizability: EVD-100 dataset (considering 12 features, 97.13% F1) and STEMI dataset (considering 3 shared features, 88.29% F1). These structured variables were subsequently used to train machine learning algorithms (Logistic Regression, XGBoost, CatBoost) for classifying amyloidosis in heart failure patients. The classifiers trained on SpaCy-structured data attained an average F1-score of 66.70%, closely matching the 66.99% F1-score from classifiers using clinician-annotated data. Conclusion This study shows the feasibility of using NLP for structuring Italian EHRs in realistic clinical settings, highlighting its potential to enhance computer-assisted detection despite selective documentation patterns. The comparable performance across annotation methods suggests NLP’s capability to bridge the gap in dataset annotation, paving the way for its integration into clinical practice.