Enhancing multi-class text classification in biomedical literature by integrating sequential and contextual learning with BERT and LSTM

Oussama Ndama, Ismail Bensassi, Safae Ndama, El Mokhtar En-Naimi

Abstract


Classification of sentences in biomedical abstracts into predefined categories is essential for enhancing readability and facilitating information retrieval in scientific literature. We propose a novel hybrid model that integrates bidirectional encoder representations from transformers (BERT) for contextual learning, long short-term memory (LSTM) for sequential processing, and sentence order information to classify sentences from biomedical abstracts. Utilizing the PubMed 200k randomized controlled trial (RCT) dataset, our model achieved an overall accuracy of 88.42%, demonstrating strong performance in identifying methods and results sections while maintaining balanced precision, recall, and F1-scores across all categories. This hybrid approach effectively captures both contextual and sequential patterns of biomedical text, offering a robust solution for improving the segmentation of scientific abstracts. The model's design promotes stability and generalization, making it an effective tool for automatic text classification and information retrieval in biomedical research. These results underscore the model's efficacy in handling overlapping categories and its significant contribution to advancing biomedical text analysis.

Keywords


Bidirectional encoder representations from transformers; Biomedical text classification; Long short-term memory; Natural language processing; Sentence segmentation

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DOI: http://doi.org/10.11591/ijece.v15i4.pp4202-4212

Copyright (c) 2025 Oussama Ndama, Ismail Bensassi, Safae Ndama, El Mokhtar En-Naimi

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES).