A TRANSFER LEARNING FRAMEWORK FOR CLINICAL TEXT CLASSIFICATION USING PRETRAINED LANGUAGE MODELS

Clinical text classification is a critical task in medical informatics, enabling applications such as automated diagnosis coding, patient outcome prediction, and adverse event detection. However, the scarcity of labeled medical data and the domain-specific language used in clinical records pose significant challenges. This paper proposes a transfer learning framework that leverages pretrained language models—specifically BioBERT and ClinicalBERT—for clinical text classification tasks. The framework incorporates a domain-adaptive fine-tuning strategy and task-specific adaptation layer to bridge the gap between general language understanding and specialized medical text. Experimental results on benchmark clinical datasets demonstrate substantial improvements in classification accuracy, F1-score, and robustness compared to traditional supervised learning approaches.