Development of a Machine Learning and NLP-Based Framework for Automated Drug Recommendations

The increasing complexity of healthcare decision-making and the growing volume of medical literature present significant challenges in identifying optimal drug therapies for patients. This study proposes the development of an integrated Machine Learning (ML) and Natural Language Processing (NLP) framework for automated drug recommendation, designed to assist healthcare professionals in clinical decision-making. The framework leverages NLP techniques to extract relevant information from unstructured medical texts, such as electronic health records, clinical notes, and research articles, while machine learning algorithms analyze patient-specific data, including demographics, medical history, and symptom profiles. By combining predictive analytics with semantic understanding, the system recommends personalized drug therapies that are clinically appropriate and contextually relevant. The framework also incorporates a feedback loop for continuous learning, enabling it to improve accuracy over time as new patient data and medical knowledge are incorporated. Experimental evaluation demonstrates that the proposed system significantly enhances the efficiency and precision of drug recommendation, reduces potential medication errors, and provides actionable insights to healthcare providers. This research highlights the potential of AI-driven solutions in transforming clinical decision support systems, promoting evidence-based medicine, and improving patient outcomes through intelligent, automated drug guidance.