ONTOLOGY-AWARE MULTIMODAL NLP FRAMEWORK INTEGRATING TRANSFORMER–CNN FUSION FOR ENHANCED MEDICAL DATA AND MANUSCRIPT ANALYSIS

The exponential growth of unstructured medical data—ranging from clinical notes and electronic health records (EHRs) to academic publications—creates substantial difficulties for automated interpretation. Conventional natural language processing (NLP) models, though useful for textual information, are limited in their ability to assimilate additional modalities such as diagnostic imaging, graphical records, and clinical diagrams, which are essential for a holistic analysis. To address this limitation, we present an ontology-guided multimodal NLP framework that integrates Transformer-based encoders for textual inputs with convolutional neural networks (CNNs) designed for medical images and diagrams. A specialized fusion layer combines these heterogeneous representations, thereby enabling context-sensitive reasoning and predictive modeling. The framework is further enhanced through the incorporation of established medical ontologies, including UMLS and SNOMED CT, which improve semantic accuracy and interpretation of domain-specific terminology.For empirical validation, the system was tested using two benchmark datasets: MIMIC-III, which provides comprehensive textual medical records, and Open-i, which offers a large collection of biomedical images. The proposed framework consistently outperformed leading baselines, achieving 97.36% accuracy, 96.82% precision, 96.59% recall, and a 96.70% F1-score. In addition to strong predictive performance, the architecture demonstrates scalability, interpretability, and compliance with stringent privacy standards, making it practical for deployment in real-world healthcare environments. By effectively unifying multimodal information with domain-specific ontological knowledge, this work delivers a computationally efficient and clinically relevant solution for advancing automated medical document analysis and decision support.