Patient Similarity Learning through Distance Metric Learning and Interactive Visualization: Jimeng Sun, IBM TJ Watson Research Center

Starts:
11:00 am on Friday, March 1, 2013
Ends:
12:00 pm on Friday, March 1, 2013
Location:
MCS 148
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.