A Transfer Learning Framework for Clinical Text Classification Using Pretrained Language Models
Year: 2025
Type: article
Abstract: 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.
Source: AI and Data Science Journal
DOI: 10.61784/adsj3018
Author: MeiLin Cheng
Institution: City University of Hong Kong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411283386