Xing Song

email Email | phone 573-884-0473

Department: Biomedical Informatics, Biostatistics and Medical Epidemiology (BBME)

Concentration: Health Informatics

My research interests are in biomedical informatics, machine learning and statistical learning algorithms, data mining and knowledge discovery. My long-term research goal is to use novel computational algorithms to discover clinically meaningful knowledge from integrated healthcare databases and apply that knowledge to understand and improve population health. I joined University of Kansas Medical Center in September 2017 for postdoctoral training in medical informatics, where I developed various machine learning models for predicting chronic kidney disease onset in the diabetic population from electronic medical records (EMRs) and a robust risk factor identification framework based on EMRs and external registries. As a Research Assistant Professor starting May 2019, I leveraged the KUMC clinical data infrastructure for research as well as providing informatics support for the PCORnet Greater Plains Collaborative (GPC), especially leading the GPC Reusable Observable Unified Study Environment (GROUSE) project. I have designed and implemented new procedures to streamline the administrative process for comprehensively managing GROUSE projects, from onboarding researchers, reinforcing annual trainings, maintaining accounts, to fulfilling various security review requirements. After completing Research Data Assistance Center (ResDAC) training on CMS data, I lead efforts in developing and refining ETL (extract, transform, and load) codes for EMR and claim data integration. I have been co-investigator or key personnel on multiple projects sponsored by regional and national grants such as BioNexus, CTSA, PCORI, NIH, which involving developing algorithms and analytical packages for cohort identification and knowledge discovery for a variety of acute or chronic health conditions including COVID-19. I joined University of Missouri as tenure-track Assistant Professor in the department of health management and informatics in December 2020. I will continue to pursue methodological advancement on mining “big” medical data and developing better predictive and prescriptive analytical models, as well as seek further infrastructural improvement for GROUSE with elastic cloud computing capability.