Title: Predictive Analytics for Clinical Decision Making
Zoran Obradovic, Laura H. Carnell

Professor of Data Analytics
Data Analytics and Biomedical Informatics Center,
Computer and Information Sciences Department,
Statistics Department
Temple University

An overview of our machine learning research aimed to facilitate decision making in healthcare will be presented in this talk. Challenges will be discussed related to integration of biomedical knowledge and heterogeneous medical records, learning from censored observations, knowledge discovery in large temporal data as well as in data obtained across multiple smaller studies. Examples of our proposed solution will be shown in the context of disease diagnosis and progression prediction in Alzheimer’s disease, Anxiety disorder, Cancer, Chronic Kidney Disease and Diabetes.

Zoran Obradovic is a Distinguished Professor and a Center director at Temple University, an Academician at the Academia Europaea (the Academy of Europe) and a Foreign Academician at the Serbian Academy of Sciences and Arts. He mentored 45 postdoctoral fellows and Ph.D. students, many of whom have independent research careers at academic institutions (e.g. Northeastern Univ., Ohio State Univ.) and industrial research labs (e.g. Amazon, Facebook, Hitachi Big Data, IBM T.J.Watson, Microsoft, Yahoo Labs, Uber, Verizon Big Data, Spotify). Zoran is the editor-in-chief at the Big Data journal and the steering committee chair for the SIAM Data Mining conference. He is also an editorial board member at 13 journals and was the general chair, program chair, or track chair for 11 international conferences. His research interests include data science and complex networks in decision support systems addressing challenges related to big, heterogeneous, spatial and temporal data analytics motivated by applications in healthcare management, power systems, earth and social sciences. For more details see http://www.dabi.temple.edu/zoran-obradovic