[Dmbu-l] Patient Similarity Learning through Distance Metric Learning and Interactive Visualization, TOMORROW 11:00 @ MCS 148

Charalampos Mavroforakis cmav at bu.edu
Thu Feb 28 14:38:32 EST 2013


Hello everyone.

Tomorrow at 11:00 in room 148 we will be hosting Dr. Jimeng Sun for a talk.
Abstract and bio are following:

Thanks,
Harry


*Title: Patient Similarity Learning through Distance Metric Learning **and
Interactive Visualization*

*Abstract:*
Heterogeneous and large volume of Electronic Health Records (EHR) data
are becoming available in many healthcare institutes. Many healthcare
applications such as clinical decision support and population
management require robust and intuitive data mining algorithms to
analyze these data. Patient similarity is a suite of such algorithms
that quantitatively measures how similar patients are to each other
based on their EHR data in a given clinical context.
I will present my research in learning patient similarity measures
that address the following challenges:
·    How to leverage physician feedback into the similarity computation?
·    How to integrate multiple sources of clinical information for
patient similarity computation?
·    How to incrementally update the existing patient similarity
functions as new data or feedback arrive?
·    How to present the similarity in an intuitive way?

I will present patient similarity learning as a core component of a
large-scale healthcare analytic research platform that we are
building. The core of the patient similarity is the combination of
novel distance metric learning algorithms and visualization
techniques. I will illustrate the effectiveness of our proposed
algorithms for patient similarity learning in several different
healthcare scenarios. Finally, I will demonstrate an interactive
visual analytic system that allows users to efficiently cluster data
and to refine the underlying patient similarity metric.

*Bio:*
Jimeng Sun is a research staff member at Healthcare Analytic
Department of IBM TJ Watson Research Center. He leads research
projects of medical informatics, especially in developing large-scale
predictive and similarity analytics on
healthcare applications.
Sun has extensive research track records on core and applied data
mining research: specialized in big data analytics, similarity metric
learning, social network analysis, predictive modeling and visual
analytics. He has published over 70 papers, filed over 20 patents (4
granted). He has received ICDM best research paper in 2007, SDM best
research paper in 2007, and KDD Dissertation runner-up award in 2008.
Sun received his B.S. and M.Phil. in Computer Science from Hong Kong
University of Science and Technology in 2002 and 2003, and PhD in
Computer Science in Carnegie Mellon University in 2007, specialized on
data mining on streams, graphs and tensor data. His advisor was Prof.
Christos Faloutsos.
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