Virtual Medicine Recommendation
Keywords:
Medicine-Medicine Interaction, Machine Learning (ML), Neural Network (NN), MMI Dataset, Medicine DatasetAbstract
According to the World Health Organization, a significant number of medication errors are caused by doctors prescribing medication based on their limited experience. However, with advancements in technology and data science techniques such as data mining and recommender systems, it is possible to analyze patient history records and assist doctors in accurately prescribing drugs. By recommending the appropriate medication based on the patient's diagnosis, we can aim to reduce experimentation and minimize adverse drug effects. In this research project, we developed a novel recommender system that provides clinical drug recommendations by considering not only symptoms but also the patient's medical record, current treatment, and if any side effects. Taking into consideration these parameters we make our system unique and even more accurate compared to a lot of other pre-existing systems, resulting in better outcomes for patients. The system's effectiveness was evaluated through extensive experimentation, and the results demonstrated its potential to improve medication safety and efficiency.
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References
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