Presented at the 2019 DAAG Conference in Denver. In Life Sciences Mini-Conference
Abstract: Pharmaceutical drug investment decisions rely heavily on data and data analytics to inform executive-level decision-making. Recently, there is a focus on exploring new products and services in the digital arena in the hopes of unlocking value and improving decision quality. The intent of machine learning and artificial intelligence is to translate relevant data and information into quantitative predictions with greater accuracy and improved efficiency to support senior management with diverse risk and value preferences. The panelists plan to discuss and explore pertinent questions, such as: For which areas within pharmaceutical drug investment decision-making would machine learning be most helpful, relevant, and effective? Traditionally human beings have the decision-making authority and responsibility; with the advent of machine learning and AI, how would DA be used in the future? Will AI eventually replace decision professionals and decision-makers?
Keywords: big data bigdata, machine learning machlearn, artificial intelligence artint, analysis and modeling anamod