A novel Approach for Biomedical Text Classification Using Deep Learning and NLP for Disease Prediction - G. S., Greeshma; Ahuja, Bindiya; Samota, Harshita; Vanitha, K.; Dubey, Alok; Mujoo, Sheetal; P. S., Saneesh (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Biomedical text classification is crucial for automating the analysis of vast biomedical literature to aid in disease prediction, given the exponential growth of biomedical data. Integrating deep learning methods with natural language processing (NLP) has revolutionized this field, offering unprecedented capabilities in understanding and extracting intricate patterns from text data coupled with advanced NLP techniques, enable researchers to uncover hidden associations between biomedical concepts, identify novel biomarkers, and enhance disease prediction accuracy. In this study, we investigate the application of deep learning and NLP for biomedical text classification, presenting a novel framework that harnesses deep neural networks to capture semantic relationships and domain-specific knowledge. Through extensive experimentation on benchmark datasets, we demonstrate the effectiveness of our approach compared to traditional machine learning methods. Our research contributes to advancing biomedical text classification, highlighting the transformative potential of deep learning and NLP in healthcare research and practice.