Xing Song, P.h.D.

Professional Background

Dr. Xing Song is currently a research assistant professor in the Division of Medical Informatics, Department of Internal Medicine at the University of Kansas Medical Center. Dr. Song received her Ph.D. from University of Missouri-Kansas City in Mathematics and Statistics. Her doctoral work was to provide analytical guidelines for designing efficient and adaptive sampling and estimation procedures based on stochastic process and Bayesian theory. During the last year of her graduate school, she worked as a predicative analytic in the Global R&D division of RGA reinsurance company, where she gained hands-on experience in building and implementing a variety of machine learning models based on large-scale and high-dimensional data, to help actuaries make pricing decisions for individual applicants.

Since joining the University of Kansas Medical center in September 2017, she has developed various machine learning models for predicting patient outcomes and a robust risk factor identification framework based on electronic medical records (EMRs) and external registries. She has been actively involved in leveraging the KUMC clinical data infrastructure for research as well as providing informatics support for the PCORnet Greater Plains Collaborative (GPC), in particular, for the Greater Plains Collaborative Reusable Observable Unified Study Environment (GROUSE), which is an integrated data environment linking insurance claims data with EMR data across GPC sites.

Research

Overview

Her research interests are in biomedical informatics, machine learning and statistical learning algorithms, data mining and knowledge discovery. Her long-term research goal to use novel computational algorithms to discover clinically meaningful knowledge from integrated healthcare database and apply that knowledge to understand and improve population health.