Relational Learning for Personalized Medicine
Monday, March 17, 2014 - 10:00am - 11:00am
KEC 1007
Sriraam Natarajan
Assistant Professor
School of Informatics and Computing
Indiana University
Abstract:
Recent advances in medicine and electronic book-keeping have greatly increased
the amount of medical data available for research andclinical decision making.
Electronic Health Records include information about test results, lab reports,
medical images, genomics,treatments, outcomes, and family histories. Together
with recent advances in data mining and machine learning, it now seems possible
to realize the grand vision of predictive personalized medicine. Statistical
Relational Learning (SRL) combines the powerful formalisms of probability
theory and first-order logic to handle uncertainty in large, complex problems.
In this talk, I illustrate the potential ofSRL to achieve an important sub-goal
of predictive medicine: early detection. Specifically, I will present SRL
approaches for (1)identifying young adults who are at high risk of developing
Coronary Heart Disease in middle and later life, and (2) identifying the set
ofpatients who have or will have Alzheimer's Disease by!
analyzing their brain MRI images. I will present a general approach for
learning SRL models based on Functional-Gradient Boosting. I will adapt this
algorithm for the above mentioned challenging tasks to produce state-of-the-art
results in three real-world medical studies. I will outline other interesting
problems in personalized medicine that we are addressing using SRL and conclude
on the optimistic note that predictive personalized medicine is within reach in
the near future.
Biography:
Sriraam Natarajan is an Assistant professor at Indiana University, USA. He was previously an Assistant Professor at Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison and had graduated with his PhD from Oregon State University. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Bio-Medical Applications. He has received the Young Investigator award from US Army Research Office. He has served on the PC of several conferences/workshops such as AAAI, IJCAI, ICML, ILP and SRL. He co-organized the AAAI 2010, the UAI 2012 and AAAI 2013 workshops on Statistical Relational AI (StarAI), ICML 2012 Workshop on Statistical Relational Learning, and the ECML PKDD 2011 and 2012 workshops on Collective Learning and Inference on Structured Data (Co-LISD). He will serve as the co-chair of the AAAI student abs!
tract and posters at AAAI 2014.
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