Fusion of Bi-GRU and temporal CNN for biomedical question classification

Abstract

Medical question classification is a crucial step in developing a highly effective question-answering system for the medical field. Accurate classification of questions plays a vital role in selecting appropriate documents for answering those questions. Deep learning models, known for their ability to uncover hidden features, have gained popularity in various natural language processing (NLP) tasks. In this study, we focus on the significance of the Temporal CNN (TCN) model in extracting insightful features from biomedical questions. We propose a novel deep learning model called Bi-GRU-TCN, which combines the advantages of Bi-GRU and TCN. This model not only captures contextual features from the Bi-GRU model but also learns spatial features through TCN layers. Through a series of experiments, we evaluate our proposed approach on benchmark datasets (BioASQ 7b and 8b) using seven deep learning models, including two ensembled models. The results demonstrate that our approach shows outstanding performance in biomedical question classification, as measured by the precision, recall, F-score, and accuracy parameters.