MediNet: ensemble transfer learning approach for classification of medical drugs-related text reviews using significant combined-embeddings.

This research work provides an innovative approach, called MediNet, for drug safety review classification that integrates the strengths of three word embedding approaches: FastText, ELMo, and GloVe, alongside an ensemble of EfficientNetB4 and MobileNet models. The unique blend of these word embeddings captures both context-independent and context-dependent representations, enabling the model to understand complex linguistic nuances within drug reviews. The ensemble architecture leverages EfficientNetB4's scalability and MobileNet's efficiency, making MediNet both powerful and resource-efficient. The proposed model MediNet is evaluated concerning performance on a comprehensive dataset of drug safety reviews, achieving remarkable results with a 95.69% accuracy, 96.46% precision, 98.30% recall, and 97.22% F1 score. The generalizability of MediNet is evaluated using the cross-validation technique, demonstrating the statistical significance of the results. Additionally, MediNet results are compared against six other well-known transfer learning models, where it consistently outperforms other models across all metrics. These results suggest that MediNet is a highly effective solution for classifying drug safety reviews, significantly improving accuracy and reliability compared to existing models. The proposed approach offers a promising direction for future research in natural language processing and its application to healthcare.