An optimized deep learning-based drug recommendation system using a sentiment analysis and transformer models for improved predictions

BACKGROUND: Healthcare users can be assisted by a medicine or drug recommendation system (MRS) by understanding their needs and supporting informed decisions based on complex knowledge. The biggest challenge is analyzing users' sentiments, as human language characterizes user-generated data in many complex ways. METHODS: This paper proposes a sentiment analysis (SA)-based drug recommender system using an optimized deep learning (DL) model with an enhanced transformer-based feature representation mechanism. The proposed system comprises four stages: preprocessing, feature extraction, classification, and medicine recommendation. Firstly, drug reviews collected from public repositories are pre-processed to improve data quality. In the feature extraction stage, the pre-processed data are fed into the Multi-Head Attention Bidirectional Encoder Representations from Transformers (MHABERT) model to capture deep semantic and contextual features. The user sentiments are then classified using Golden Jackal Optimized Bidirectional Long Short-Term Memory (GOBLSTM). Finally, medicine recommendations are generated by comparing the similarities of drug reviews. RESULTS: The Kaggle medicine recommendation and Yelp health datasets are used to evaluate the proposed method. Experimental results demonstrate maximum accuracies of 98.43% and 98.30% on the Kaggle and Yelp datasets, respectively, outperforming existing approaches. CONCLUSION: The combination of SA and drug recommendation enhances patient outcomes, optimizes medicine selection, and reduces adverse reactions.