Published Paper Details:

As a result of the coronavirus, access to legitimate clinical resources has worsened significantly, including shortages of specialists and health workers, insufficient equipment, and drug shortages. Due to the emergency of the entire medical community, many individuals died. Individuals started taking medication themselves without proper consultation due to unavailability, which made their health condition more serious than usual. A growing number of applications are using machine learning and innovative work is being done in automation. In this project, a drug recommendation system is presented with the aim of significantly reducing the burden on specialists. Using patient reviews, we developed a drug recommendation system that uses various vectorization processes such as Bow, TF-IDF, Word2Vec, and Manual Feature Analysis to predict sentiment, which can be used to recommend the most appropriate drug for a given disease based on different classification algorithms. AUC, precision, F1 score and accuracy were used to evaluate the predicted feelings. In emergency situations such as pandemics, floods or cyclones, a medical referral system can help. In the era of machine learning (ML), recommender systems produce more accurate, faster, and more reliable clinical predictions at minimal cost. As a result, these systems maintain better performance, integrity and privacy of patient data in the decision-making process and provide accurate information at all times. Therefore, we present drug recommendation systems to improve the equity and safety of infectious disease treatment. To reduce side effects, medications are recommended based on the patient's previous health profile, lifestyle and habits. A system like this could be useful in recommending safe drugs to patients, especially during medical emergencies