Few-shot medical relation extraction via prompt tuning enhanced pre-trained language model

Medical relation extraction is crucial for developing structured information to support intelligent healthcare systems. However, acquiring large volumes of labeled medical data is challenging due to the specialized nature of medical knowledge and privacy constraints. To address this, we propose a prompt-enhanced few-shot relation extraction (FSRE) model that leverages few-shot and prompt learning techniques to improve performance with minimal data. Our approach introduces a hard prompt concatenated to the original input, enabling contextually enriched learning. We calculate prototype representations by averaging the intermediate states of each relation class in the support set, and classify relations by finding the shortest distance between the query instance and class prototypes. We evaluate our model against existing deep learning based FSRE models using three biomedical datasets: the 2010 i2b2/VA challenge dataset, the CHEMPROT corpus, and the BioRED dataset, focusing on few-shot scenarios with limited training data. Our model demonstrates exceptional performance, achieving the highest accuracy across all datasets in most training configurations under a 3-way-5-shot condition and significantly surpassing the current state-of-the-art. Particularly, it achieves improvements ranging from 1.25% to 11.25% on the 2010 i2b2/VA challenge dataset, 3.4% to 20.2% on the CHEMPROT dataset, and 2.73% to 10.98% on the BioRED dataset compared to existing models. These substantial gains highlight the model’s robust generalization ability, enabling it to effectively handle previously unseen relations during testing. The demonstrated effectiveness of this approach underscores its potential for diverse medical applications, particularly in scenarios where acquiring extensive labeled data is challenging.