Abstract
Healthcare systems increasingly depend on the structured exchange of information between hospitals, laboratories, and digital platforms. The HL7 v2.x standard provides the backbone for this communication but remains challenging for machine interpretation because of its variable syntax and optional segments. To address this limitation, a hybrid artificial intelligence model was developed for automated processing and classification of HL7 messages, integrating both structural learning and semantic validation. The experimental workflow included the generation of a synthetic dataset of 3,000 patient lifecycles with more than 7,000 ADT messages, followed by parsing, feature engineering, and supervised training. Logistic Regression, Random Forest, and Gradient Boosting were evaluated as baseline classifiers, while a semantic layer combining Named Entity Recognition and Regular Expressions introduced context-aware features such as physician names, medical facilities, and diagnosis indicators. After retraining, ensemble models demonstrated measurable improvement, with Random Forest achieving an increase of +9.3 % in accuracy and +7.0 % in F1-score. The results confirm that the addition of semantic cues enhances model interpretability and overall robustness, bridging the gap between structured message parsing and naturallanguage understanding. The proposed hybrid pipeline may serve as a foundation for intelligent interoperability solutions and future FHIR-compatible healthcare data systems.