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.

KEYWORDS:

• Bi-GRU
• ensemble learning model
• question classification
• temporal CNN
• Word2Vec

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The reference links of the datasets used to support the novelty and findings of our work are included within the article.

Additional information

Notes on contributors

Tanu Gupta

Tanu Gupta is a PhD Candidate in Indira Gandhi Delhi Technical University for Women, India. She received a B.Tech degree (2014) in Information Technology and an M.Tech degree (2017) in Computer Science and Engineering from Guru Gobind Singh Indraprastha University, India. She joined Indira Gandhi Delhi Technical University for Women after Post Graduation. Her research interests include Natural Language Processing, Semantic Web and deep learning. She has done research work in healthcare data and also published papers in good journals. She is the member of Computer Society of India and Soft Computing Research Society.

Ela Kumar

Prof. Ela Kumar is a professor in Indira Gandhi Delhi Technical University for Women, India. She received the b.tech degree (Electronics and Communication) in 1988 and M.tech degree (Computer Science and Technology) in 1990 from IIT Roorkee. She has been awarded Ph.D (Computer Science) in 2004 from Delhi University. She has received her PhD in Natural Language Processing from Delhi University, India in 2004 and M.Tech in Computer Science and Technology in 1990 from IIT Roorkee, India. She has more than 100 remarkable publications in the areas of knowledge engineering, expert systems and deep learning. Currently, she is Professor and Dean of CSE Department in Indira Gandhi Delhi Technical University for Women, India. She is member of many professional societies like IEEE, CSI, ISTE and others.