Drug Recommendation System for Healthcare Professionals’ Decision-Making Using Opinion Mining and Machine Learning

The concern has been raised regarding errors in drugs prescription and medical diagnostics that need to be carefully thought through. Both patient diagnosis and medication prescription are the responsibilities of healthcare providers. As the number of people with health issues rises, the healthcare professionals’ burden is increased. Medical errors may occur in the healthcare sector as a result of healthcare professionals prescribing drugs medicines based on inadequate information related to patient history and drug side effects. Therefore, this study aims to propose a drug recommender system to assist healthcare providers in decision making when prescribing drugs for patients depending on their diagnoses. Drug reviews sentiments are analyzed to find the drug effectiveness among the users. Furthermore, the most suitable recommender algorithm for recommending drugs based on the data from healthcare professionals are selected for this study. Opinion mining is applied on drug reviews, and a hybrid method is implemented to overcome the limitations of content-based and collaborative filtering methods, such as the cold start problem and increasing client preference. The system is developed and tested successfully. The proposed system can assist healthcare professionals in drug decision making and sustain the whole digital care pathway for various diseases.