Building Effective Drug Recommender Systems with Sentiment Analysis and Patient Feedback.

The study presents a machine learning-based drug recommendation system designed to reduce medical prescription errors by leveraging patient reviews and sentiment analysis. By integrating data from various sources, the system predicts drug effectiveness and suggests the best medication for specific medical conditions. The approach combines natural language processing, supervised learning, and data analysis to extract insights from user feedback. Experimental results demonstrate the system's capability to enhance decision-making in drug prescriptions, ensuring improved patient outcomes. The paper also outlines limitations and proposes directions for future work, such as expanding datasets and improving real-time adaptability. The increasing reliance on digital platforms for healthcare guidance has highlighted the potential of machine learning in reducing medical prescription errors. This study proposes a drug recommendation system that uses patient reviews and sentiment analysis to suggest appropriate medications for specific conditions. Leveraging datasets with over 160,000 reviews, the system employs natural language processing and machine learning algorithms to classify sentiment and rank drugs based on effectiveness. Experimental results validate the system's ability to provide accurate recommendations, demonstrating its potential to enhance healthcare delivery, reduce prescription errors, and improve patient outcomes.