Machine learning for decision making in health care
Presented by Zoran Obradovic, Temple University
Monday, December 10, 2018
Brickyard (BYENG) 210, Tempe campus [map]
An overview of our ongoing projects aimed to facilitate predictive analytics in healthcare will be presented in this talk. Challenges and the proposed solutions will be discussed related to structured regression on multilayer networks, recovering network connectivity, modeling positive and negative influences, uncertainty propagation and effective integration of domain knowledge and big data. The algorithms will be evaluated in the context of applications related to exploiting information extracted from electronic health records for identifying resources a patient would need for triage systems in emergency departments, estimating hospitalization cost, predicting admission and mortality rate for high impact diseases, identifying disease relationships, discovering gene-disease interactions and assessing tolerance to viral infections.
About the speaker
Zoran Obradovic is an academician at the Academia Europaea (the Academy of Europe) and a foreign academician at the Serbian Academy of Sciences and Arts. He is an L.H. Carnell Professor of Data Analytics at Temple University, professor in the Department of Computer and Information Sciences with a secondary appointment in the Department of Statistical Science and is the director of the Center for Data Analytics and Biomedical Informatics. His research interests include data science and complex networks in decision support systems.
Obradovic is the editor-in-chief at the Big Data journal and the steering committee co-chair for the SIAM Data Mining conference. He is also the editorial board member at 13 journals and was the general chair, program chair or track chair for 11 international conferences. He has published more than 370 articles and is cited more than 22,000 times (H-index 54).