Predicting Drug Interactions Using Machine Learning Patients taking multiple medications be it prescription or over-the-counter tend to experience more adverse events. A recent survey show that simultaneous use of different products affects about half of the elderly populations with regards to increasing risk of morbidity. The goal of this solution is to develop a predictive model to determine leading indicators of drugs that are likely to interact with other drugs when administered within a specified period. The use of de-identified patient level data including demographics, prescribers, conditions and diseases as well as current and historical medications could be mined and explored to develop a novel solution. The intention is to use a mixture of publicly available data sources and real-world data in this model. Several stratifications and scenarios will be considered in the initial exploratory data analysis prior to using supervised and unsupervised learning model including ensembles to arrive at final suitable model. We will factor in groupings by similar diseases, co-morbidities, consider drug metabolism, route of administration and molecule class. A propensity or risk score will be calculated as an outcome. A data driven method of identifying drug interactions that will enhance efforts at efficacy of drugs and reduce morbidity is the objective here. The benefits include but are not limited to improving patient qualify of life, enhanced understanding of compounds, indications, contraindications for manufacturers. The final model in this solution will have a high measure of accuracy.