Adapter Based on Pre-Trained Language Models for Classification of Medical Text

Abstract

We present an approach to classify medical text at a sentence level automatically. Given the inherent complexity of medical text classification, we employ adapters based on pre-trained language models to extract information from medical text, facilitating more accurate classification while minimizing the number of trainable parameters. Extensive experiments conducted on various datasets demonstrate the effectiveness of our approach.