An Improved Drug Recommendation System Using Artificial Intelligence Assisted Learning Methodology

In order to help doctors and patients make better judgments regarding medication prescriptions and treatments, the Drug Recommender System uses sentiment analysis powered by machine learning and this cutting-edge method examines the feelings and attitudes of users in text data by use of big data and advanced analytics. The technology offers helpful insights on pharmaceutical efficacy, side effects, and patient satisfaction by extracting and categorizing attitudes using machine learning algorithms. Deep learning, automation, and artificial intelligence (AI) are all seeing increased use in a wide variety of applications. This study presents a medication suggestion system with the goal of greatly alleviating the workload of experts. The research presents a new AI-based learning method, the Artificial Intelligence based Neural Drug Suggestion Model (AINDSM), and uses a cross-validation with the traditional Support Vector Machine (SVM) model to assess the effectiveness of the suggested scheme. A medical referral system can be of great assistance in times of disaster, such pandemics, floods, or cyclones. Now that we live in the ML age, recommender systems can provide low-cost, high-quality clinical predictions in record time. Therefore, these systems always give correct information while also maintaining superior performance, data integrity, and privacy for patients' medical records throughout decision-making. As a result, we offer medication recommendation tools to make infectious illness treatment more fair and safe. The patient's past health history, way of life, and habits are taken into account while recommending medicine with the goal of minimizing adverse effects. In times of medical crisis, a system such as this might help prescribe patients safe medications.