Xingyu (Mark) Zhang, Ph.D.

Xingyu (Mark) Zhang

Research Assistant Professor
Department of Systems, Populations and Leadership
Room 1177, 400 North Ingalls

University of Michigan School of Nursing
400 North Ingalls Building
Ann Arbor, MI 48109-5482

Telephone: (734) 763-0930


  • Health outcomes with an emphasis on study design and statistical analysis
  • Electronic health record data mining
  • Predictive and diagnostic modeling in healthcare
  • Emergency heath care and health service
  • Public health surveillance modeling

Xingyu Zhang is a Research Assistant Professor at the School of Nursing’s Applied Biostatistics Laboratory (ABL).  He received his Ph.D. in Biomedical Science concentrated on biostatistics from the University of Auckland in 2016.  Prior to joining the ABL, he was a postdoctoral research fellow in epidemiology and biostatistics at Emory University, and also a visiting research scholar in medical informatics at Georgia Institute of Technology. Dr. Zhang’s research focuses on healthcare outcomes with an emphasis on study design and statistical analysis. The methods he applied include multi-level analysis, time series analysis, infection early warning modeling, medical imaging analysis, feature extraction, pattern classification, neural networks, support vector machine, natural language processing, deep learning, survival analysis, meta-analysis, etc.  

Affiliations / Service

  • Member, American Statistical Association, 2018-present
  • Member, International Society for Infectious Diseases (ISID), 2018-present

Notable Awards / Honors

  • Postdoc Research Fellow, Emory University School of Medicine, 2016-2018
  • Visiting Scholar, Georgia Institute of Technology College of Computing, 2017-2018


  • PhD, University of Auckland, Auckland, New Zealand, IN 2016
  • MS, Sichuan University, Chengdu,China, IN 2013
  • BS, Binzhou Medical University, Yantai, China, IN 2010

Publication Highlights

  • Zhang, X., Carabello, M., Hill, T., He, K., Friese, C. R., & Mahajan, P. (2019). Racial and Ethnic Disparities in Emergency Department Care and Health Outcomes Among Children in the United States. Frontiers in Pediatrics, 7, 525.

  • Zhang, X., Bellolio, M. F., Medrano-Gracia, P., Werys, K., Yang, S., & Mahajan, P. (2019). Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department. BMC Medical Informatics and Decision Making, 19(1), 287.

  • Gander, J. C., Zhang, X., Ross, K., Wilk, A. S., McPherson, L., Browne, T., ... & Patzer, R. E. (2019). Association between dialysis facility ownership and access to kidney transplantation. Jama, 322(10), 957-973.

  • Fan, H., Liu, Y., & Zhang, X. (2019). Validation of recommended definition in identifying elevated blood pressure in adolescents. The Journal of Clinical Hypertension, 21(9), 1343-1349.

  • Zhang, X., Kim, J., Patzer, R. E., Pitts, S. R., Chokshi, F. H., & Schrager, J. D. (2019). Advanced diagnostic imaging utilization during emergency department visits in the United States: A predictive modeling study for emergency department triage. PloS one, 14(4), e0214905.

  • Schrager, J., Patzer, R., Kim, J., Pitts, S., Chokshi, F., Phillips, J., & Zhang, X. (2019). Racial and Ethnic Differences in Diagnostic Imaging Utilization During Adult Emergency Department Visits in the United States, 2005 to 2014. Journal of the American College of Radiology.

  • Zhang, X., Melanson, T. A., Plantinga, L. C., Basu, M., Pastan, S. O., Mohan, S., . . . Patzer, R. E. (2018). Racial/ethnic disparities in waitlisting for deceased donor kidney transplantation 1 year after implementation of the new national kidney allocation system. American Journal of Transplantation.

  • Zhang, X., & Liu, Y. C. (2018). The resurgence of scarlet fever in China. The Lancet Infectious Diseases, 18(8), 823-824.

  • Zhang, X., Kim, J., Patzer, R. E., Pitts, S. R., Patzer, A., & Schrager, J. D. (2017). Prediction of emergency department hospital admission based on natural language processing and neural networks. Methods of Information in Medicine, 56(05), 377-389.

  • Zhang, X., Medrano-Gracia, P., Ambale-Venkatesh, B., Bluemke, D. A., Cowan, B. R., Finn, J. P., . . . Young, A. A. (2017). Orthogonal decomposition of left ventricular remodeling in myocardial infarction. GigaScience, 6(3), 1-15.

  • Zhang, X., Hou, F., Li, X., Zhou, L., Liu, Y., & Zhang, T. (2016). Study of surveillance data for class B notifiable disease in China from 2005 to 2014. International Journal of Infectious Diseases, 48, 7-13.

  • Zhang, X., Hou, F., Qiao, Z., Li, X., Zhou, L., Liu, Y., & Zhang, T. (2016). Temporal and long-term trend analysis of class C notifiable diseases in China from 2009 to 2014. BMJ open, 6(10), e011038.

  • Zhang, X., Zhang, T., Pei, J., Liu, Y., Li, X., & Medrano-Gracia, P. (2016). Time series modelling of syphilis incidence in China from 2005 to 2012. PLoS One, 11(2), e0149401.

  • Zhang, X., Ambale-Venkatesh, B., Bluemke, D. A., Cowan, B. R., Finn, J. P., Kadish, A. H., . . . Suinesiaputra, A. (2015). Information maximizing component analysis of left ventricular remodeling due to myocardial infarction. Journal of translational medicine, 13(1), 343.

  • Zhang, X., Zhang, T., Young, A. A., & Li, X. (2014). Applications and comparisons of four time series models in epidemiological surveillance data. PLoS One, 9(2), e88075.