THE ROLE OF COMPUTATIONAL LINGUISTIC MODELING, NATURAL LANGUAGE PROCESSING (NLP), AND ONTOLOGY-BASED SEMANTIC ANALYSIS IN THE MANAGEMENT OF MODERN PHARMACEUTICAL TERMINOLOGY
Keywords:
Computational linguistics, pharmaceutical terminology, natural language processing (NLP), standardization, unification, semantic modeling, ontology, multilingual healthcare communication, digital healthcare, pharmacovigilance.Abstract
This study investigates the role of computational linguistic modeling, natural language processing (NLP), and ontology-based semantic analysis in the management of modern pharmaceutical terminology. The research focuses on the systematic extraction, classification, standardization, and unification of over 12,000 pharmaceutical terms sourced from scientific publications, regulatory documents, electronic health records, and pharmaceutical dictionaries. Computational methods allowed the identification of synonyms, polysemous terms, and morphological variants, while alignment with international standards such as WHO INN, ATC, and MedDRA ensured terminological consistency and interoperability across languages and healthcare systems. The study demonstrates that these approaches enhance precision, accuracy, and accessibility of pharmaceutical information, support multilingual communication, and improve global collaboration in research and clinical practice. Additionally, dynamic updates of emerging terms through AI-driven systems contribute to patient safety, pharmacovigilance, and efficient digital healthcare management. The findings confirm that integrating computational linguistics into pharmaceutical terminology is a pivotal step toward a standardized, coherent, and adaptable system for modern healthcare communication.Downloads
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