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Abstract : In critical medical emergencies, timely and accurate drug recommendation is essential for saving lives and reducing complications. This project proposes a Drug Recommendation System utilizing Machine Learning (ML) techniques to assist healthcare professionals in making quick and accurate drug selections based on patient symptoms, medical history, and emergency condition. The system integrates data from diverse medical databases, including symptoms, diseases, patient demographics, and prior medical records, to recommend the most appropriate drugs or treatments in real-time. The ML model is trained on historical medical data, utilizing algorithms such as Decision Trees, Random Forests, and Neural Networks to predict optimal drug choices for different emergency situations. The system not only considers the immediate medical needs but also factors in contraindications, potential drug interactions, and patient-specific conditions, ensuring safer drug administration. This approach aims to reduce human errors in emergency drug prescriptions, enhance the speed of medical response, and improve the overall quality of care during emergencies. By leveraging data-driven insights, the system supports healthcare professionals in making well-informed decisions, even in high-pressure situations. Ultimately, the Drug Recommendation System serves as a valuable tool in healthcare, enhancing emergency medical care and optimizing treatment outcomes.
Keywords: Drug Recommendation System, Medical Emergencies, Machine Learning, Real-time Decision Support, Healthcare, Decision Trees, Random Forests, Neural Networks, Drug Interactions, Clinical Decision Support.