Nima Latifsoltojar
Amirkabir University of Technology, Iran
Title: Artificial intelligence clustering for finding indirect drug competitions based on RxNorm Terminology
Biography
Biography: Nima Latifsoltojar
Abstract
Artificial intelligence clustering for finding indirect drug competitions based on RxNorm terminology: market analysis seeks to understand the dynamics of markets, with the goal of informing business strategies and drug pipeline. Analysis of the current and potential future markets for drugs to treat particular diseases is used by pharmaceutical companies in decisions about where to invest in drug research and development. The first step to realizing the market is finding the drug rivals. We define two kind of rivalry between drugs, direct and indirect. The indirect drug rivals does not have a same generic but in a prescription they can be written instead of each other by physician, for a same disease and same level of care. In our analysis we selected a group of oncology drugs from RxNorm (version: 05-Aug-2019) and used the neural network clustering method and indication, Anatomical Therapeutic Chemical (ATC) levels and physiologic effects as features for finding the rivals. All iterations of results shared with five oncologists. We found the groups of drugs with different generic name, but same role in drug market competition. However, cause of missing data in RxTerm this method cannot be executed for all drugs.