A Comprehensive Approach for Healthcare Decision-Making Through Integrated Data Mining and NLP-Enhanced Drug Recommendation Systems

Integrating artificial intelligence (AI) is crucial for addressing challenges in healthcare, particularly in medical data analysis and drug recommendations. This paper presents two methodologies to improve healthcare decision-making using data mining and AI-driven drug recommendation systems. The first method employs data mining, sentiment analysis with the Vader tool, and Natural Language Processing (NLP) on extensive medical datasets from Hospital Information Systems (HIS). It accurately predicts diseases and offers personalized drug recommendations based on data insights, enhancing precision with a weighted average approach. The second method highlights AI’s importance in drug recommendation systems, addressing the challenge of staying updated on the latest treatments. We developed a system using NLP and Machine Learning (ML) algorithms to predict medical conditions and recommend drugs based on reviews and their usefulness. Simple symptom input provides individuals with information on their disease and helpful drugs. These methodologies significantly advance healthcare decision-making, with sentiment analysis capturing patient experiences. Experimental results demonstrate these methods effectively enhance healthcare decision-making, improving patient outcomes and efficiency.