报告题目：Integrating Predictive Analytics (Machine Learning) and Prescriptive Analytics (Optimal Decision Making) – A Review
报告人：CHEN Youhua (Frank) ，City University of Hong Kong
报告摘要：Historically, research in decision making under uncertainty in operations research (OR) has focused on models and their optimization. In an ideal setting, optimization was substantiated by estimating the model parameters, such as distributions, based on actual data. Machine learning (ML) has been developed to predict the quantity of interest (predictive analytics). However, ML does not generally address optimal decision-making under uncertainty that is appropriate for OR problems (prescriptive analytics). In recent years, a new approach emerges, i.e., combining ideas from ML and OR in developing a framework for using data to prescribe optimal decisions in OR problems. In this talk, we first give a review on this development and then show an on-going research project in a healthcare management application. In the project, we intend (1) to develop a ML predictive model to early identify high-risk subpopulation for further follow-up interventions, and (2) to use the predictive model to guide allocation of limited health care resources to balance efficiency - readmission reduction and health outcomes of patients.
报告人简介：CHEN Youhua (Frank) is Chair Professor and Head of Department of Management Sciences, College of Business, City University of Hong Kong. He holds a bachelor degree in Engineering, master degree in Economics, and doctoral degree in Management from Tsinghua University, the University of Waterloo, and the University of Toronto, respectively. His current research interests include data-driven healthcare management and resource planning, logistics/supply chain management, and operations management. He is currently leading a Theme-based Research Scheme (TRS) project “Delivering 21st Century Healthcare in Hong Kong – Building a Quality-and-Efficiency Driven System” (Hong Kong Research Granting Council), which has been participated by members from multiple local and overseas universities. His papers have appeared in journals such as Operations Research, Management Science and Production and Operations Management.