Modeling Patient's Response in Acute Inflammation Treatment
Monday, May 19, 2014
2:00 pm - 3:00 pm
Gross Hall 330
Zoran Obradovic, Temple University
Uncontrolled inflammation accompanied by an infection that results in septic shock is a common cause of death in intensive care units and one of the leading cause of death overall. In principle, spectacular mortality rate reduction can be achieved by early diagnosis and accurate prediction of response to therapy. This is a very difficult objective due to the fast progression and complex multi-stage nature of acute inflammation. Our ongoing DARPA DLT project is addressing this challenge by development and validation of effective predictive modeling technology for analysis of temporal dependencies in multi-dimensional sepsis related data. This lecture will provide an overview of the results of our project, which show potentials for significant mortality reduction in severe sepsis patients. Zoran Obradovic is a L.H. Carnell Prof of Data Analytics at Temple University, Prof-Dept of Computer and Information Sciences with a secondary appt in Statistics, and is the Director-Center for Data Analytics and Biomedical Informatics. His research interests include data mining and complex networks applications in health mgmt. Zoran is the executive editor at the journal on Statistical Analysis and Data Mining, which is the official publication of the American Statistical Association and is an editorial board member at 11 journals. He was general co-chair for 2013 and 2014 SIAM Intl Conf on Data Mining and was the program or track chair at many data mining and biomed informatics conf.