Hybrid Transformer-Based Framework for Clinical Text Mining and Automated Medical Report Analysis

Abstract

The rapid digitization of healthcare systems has resulted in the generation of large volumes of unstructured clinical text, including medical reports, prescriptions, and physician notes. This paper presents a Hybrid Transformer-Based Framework for Clinical Text Mining and Automated Medical Report Analysis. The proposed system leverages advanced transformer architectures to extract meaningful insights from unstructured medical text. Pre-trained language models such as BERT are fine-tuned on domain-specific datasets to perform tasks including clinical entity recognition, relation extraction, and automated summarization. A hybrid approach combining rule-based filtering and deep learning is employed to enhance accuracy and reduce noise in extracted information. The framework supports real-time processing and can be integrated with electronic health record systems for automated documentation and decision support. Additionally, attention mechanisms are utilized to improve interpretability and highlight critical medical terms. Experimental results demonstrate superior performance compared to traditional natural language processing techniques. Integration with prior research in healthcare analytics enhances robustness and scalability. The study highlights the potential of transformer-based models in enabling efficient, accurate, and intelligent clinical text analysis for modern healthcare systems.