Wednesday, October 9, 2019 - 1:30pm to 2:30pm
ISyE Groseclose Room 402
Title: Knowledge-infused global-local data fusion for precision medicine and beyond
Medicine has evolved from one-treatment-fits-all to personalized medicine that takes inter-person difference into account for treatment. Recent development in cancer medicine is pushing this evolution to a new paradigm: heterogeneity exists not only between patients but also within each tumor. Intra-tumor molecular heterogeneity has been found to be a leading cause of treatment failure for some aggressive cancers. This urges the need for mapping out regional molecular status across each tumor. It is a challenging task because no single data source can accomplish this goal. Localized biopsy samples are scarce due to their invasive nature. Imaging portrays the entire tumor but does not provide direct measure of molecular characteristics. Also available are knowledge-based mechanistic models, which may be inaccurate or incomplete. Fusion of all these data/knowledge sources provides the best promise for mapping out intra-tumor molecular heterogeneity and ultimately enabling new treatment with unprecedented precision.
In this talk, I will introduce a modeling framework allowing for knowledge-infused global (imaging)-local (biopsy) data fusion. This framework has roots in semi-supervised learning but innovates through mechanistic model integration and simultaneous feature & instance selection. An extension of this model will also be discussed, which allows for uncertainty quantification. Case studies of glioblastoma – the most aggressive type of brain cancer – are presented with real data, demonstrating the potentially transformative impact on treatment (surgical resection, radiation therapy, genetic therapy).
Interestingly, we found that the need for knowledge-infused global-local data fusion exists beyond medicine, such as early detection of forest fires and mapping out regional poverty in the developing world. It will be interesting to test the utility of the proposed methodology in these applications.
Dr. Jing Li is an Associate Professor in Industrial Engineering & Computer Engineering at Arizona State University (https://www.public.asu.edu/~jli09/). She received her B.S. from Tsinghua University, and an M.A. in Statistics and a Ph.D. in Industrial and Operations Engineering from the University of Michigan. Her research interests are data fusion and statistical machine learning intersecting with health care domains having complex data structures. Dr. Li’s research is sponsored by NIH, NSF, DOD, Arizona State, Mayo Clinic, and biomedical industry. She co-founded the ASU-Mayo Clinic Center for Innovative Imaging, conducting various collaborative projects with the Departments of Radiology, Neurology, Neurosurgery, and Radiation Oncology at Mayo Clinic. She is an NSF CAREER awardee, a recipient of a Best Paper Award and a Best Application Paper Award from IISE Transactions, a recipient of the Harold Wolff-John Graham Award (Best Paper) from the American Academy of Neurology, and a recipient of the Harold G. Wolff Lecture Award (Best Paper) by the American Headache Society. She is a former Chair for the Data Mining Subdivision of INFORMS. She is currently the Editor-in-Chief for Quality Technology and Quantitative Management, an Associate Editor for IISE Transactions on Healthcare Systems Engineering, and on the editorial board of Journal of Quality Technology.