Title: Decision Support for Healthcare.
Abstract:
Decision support using models learned or constructed through knowledge aggregation, often broadly referred to as Artificial Intelligence, is essential the improvement of patient healthcare pathway. These tools, whether built from a large dataset or through symbolic modeling of one or multiple expertises, can serve as diagnostic help for practitioners, also support the optimization of healthcare organization, or enable active patient monitoring. Ultimately, the goal is to provide better
patient care while managing and minimizing available resources. In this presentation, we will discuss the use case of managing and optimizing the resources of a hospital emergency service. This will not only involve the design of a decision support model but also explore data anonymization and imputation of missing data.
Biography:
Prof. Gilles Dequen is a French researcher from the MIS Lab, Université de Picardie Jules Verne (UPJV). He received a Ph.D. degree in Computer Science in 2001 and subsequently the HDR degree in 2011. He has been CNRS (French National Scientific Research Center) delegate at the CNRS-LIP6 Laboratory from 2009 to 2010. Gilles Dequen is a Full Professor at UPJV since 2013 and leads the MIS Lab since 2017. Prof. Dequen teaches at UFR des Sciences of UPJV within the Computer Science department in Bachelor and MSc degrees. Mainly, his research interests are centered on Numerical Modeling on one hand, and Decision and Optimization problem solving on the other hand. He applies his expertise in modeling and solving applications in cybersecurity and healthcare. He is also a founder member of the GRECO Institute dedicated to robotic surgery. He is a PC member of several AI Conferences such as IJCAI-ECAI, ICTAI, SAT, etc., and a designated expert supporting several French Government research programs.