[CSEE Talk] talk: Large scale predictive modeling on electronic health records, 1pm Wed 3/26, UMBC

[Tim Finin]

              Computer Science and Electrical Engineering
                University of Maryland, Baltimre County

             FEATURE ENGINEERING FOR LARGE SCALE PREDICTIVE
                MODELING WITH ELECTRONIC HEALTH RECORDS

                              Dr. Fei Wang
                  Healthcare Analytics Research group
                    IBM T. J. Watson Research Center

             1:00pm Wednesday, 26 March 2014, ITE325b, UMBC

Predictive modeling lies in the heart of many medical informatics
problems, such as early detection of some chronic diseases and patient
hospitalization/readmission prediction. Typically those predictive
models are built upon patient Electronic Health Records (EHR), which
are systematic collection of patient information including
demographics, diagnosis, medication, lab tests, etc. We refer those
information as patient features.  High quality features are of vital
importance to building successful predictive models. In this talk, I
will present two feature engineering technologies to improve the
quality of the raw features extracted from original patient EHRs: (1)
feature augmentation, which constructs more effective derived features
from existing raw features by exploring the event sequentiality; (2)
feature densification, which imputes the missing feature values via
knowledge transfer across similar patients. Along with each technique
we also developed a visual interface to facilitate the user exploring
the derived features. Finally I will conclude the whole talk with some
future research directions.

Dr. Fei Wang is currently a research staff member in Healthcare
Analytics Research group, IBM T. J. Watson Research Center. Before his
current position he was a postdoc in Department of Statistical
Science, Cornell University. He received his Ph.D. from Department of
Automation, Tsinghua University in 2008. Dr. Wang's major research
interests include data mining, machine learning as well as their
applications in social and health informatics. He actively publishes
papers on the top venues of the relevant fields including AMIA, KDD,
ICML and InfoVis, and he has filed over 20 patents (four issued).

Dr. Wang has given seven tutorials on different topics at
ICDM/SDM/ICDM, organized seven workshops on KDD/ICDM/SDM/WSDM, and
edited three special issues on Journal of Data Mining and Knowledge
Discovery. His Ph.D.  thesis was awarded the National Excellent
Doctoral Thesis in China. His research paper was selected as the
recipient of the Honorable mention of the best research paper award in
ICDM 2010, and best research paper finalist in SDM 2011. More
information can be found on his homepage at
https://sites.google.com/site/feiwang03/.

Host: Prof. Kostas Kalpakis, kalpakis at umbc.edu

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