Juan (Wendy) Zhao, PhD

Research Assistant Professor

Deep phenotyping using unsupervised machine learning

Tensor factorization on temporal structured EHR for deep phenotyping
We applied a state-of-arts tensor factorization method to longitudinal EHR data and identify 14 clinically relevant subphenotypes for CVD. We found that some phenotypes such as Vitamin D deficiency and depression, and Urinary infections were not captured by the conventional risk scores, and the top six prevalent subphenotypes differ in the risk of developing the subsequent myocardial infarction.


Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study

Juan Zhao, Yun Zhang, David J. Schlueter, Patrick Wu, Vern Eric Kerchberger, S. Trent Rosenbloom, Quinn S. Wells, QiPing Feng, Joshua C. Denny, Wei-Qi Wei

Journal of Biomedical Informatics, vol. 98, 2019 Oct, p. 103270


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