Recurrent Neural Networks based Bi-LSTM with Sentiment Analysis and Transfer Learning approach for Medicine Recommendation System

The implementation of an efficient medication recommendation system is indispensable for personalized medicine to improve patient outcomes by providing personalized treatment recommendations. This paper presents a comprehensive framework for medication recommendation based on transfer learning using deep learning methods like Recurrent Neural Networks (RNNs) and Bidirectional Long Short Term Memory networks (Bi-LSTM) along with sentiment analysis of user reviews for opinion mining. The proposed method implements a complex-sequential modeling architecture using an RNN-BiLSTM-driven deep network to model the complex sequential patterns and timing relationships among drug-related data, thereby preparing the system for learning and eventual adaptation based on past usage and feedback. Bi-LSTM retains context-information in both forward and reversed directions to attain better context understanding. Transfer learning implementation of the framework gains the knowledge from pre trained model pertaining to medical recommendation system towards ensuring high generalizability of the developed model. The proposed method essentially binds insights from historical users' data, user sentiments, and advanced deep learning to satisfy patients and treatment outcomes towards a more precise and personalized system for drug recommendations. The dataset required for conducting the experiment was gathered from the Kaggle repository. The experimental results have been validated by various measures such as ROC rate, precision, F1 Score, confusion matrix, and accuracy. The experimental results showed that the proposed method achieved a 97.6% accuracy using the measures considered, which is significantly better than traditional machine learning and deep learning algorithms.