Juan (Wendy) Zhao, PhD

Research Assistant Professor

Juan (Wendy) Zhao, PhD

I am a Research Assistant Professor in the Department of Biomedical Informatics (DBMI) at Vanderbilt University Medical Center (VUMC). I earned my Ph.D. in Computer Science (2012) from the Chinese Academy of Sciences. 
My research interests include leveraging AI, machine learning, data mining, natural language processing (NLP), and blockchain for enhancing precision medicine, with an emphasis on developing new algorithms on large health data to improve deep phenotyping, uncover knowledge, and improve accuracy and fairness for disease prediction. I am interested in research topics including model explanation, uncertainty measurement, and fairness. 


Integration of Omics and Phenotypic Data for Precision Medicine

Juan Zhao, QiPing Feng, Wei-Qi Wei

Methods in Molecular Biology (Clifton, N.J.), vol. 2486, 2022, pp. 19--35

Identifying Potential Therapeutic Applications and Diagnostic Harms of Increased Bilirubin Concentrations: A Clinical and Genetic Approach

Jacy T. Zanussi, Juan Zhao, Chad A. Dorn, Ge Liu, QiPing Feng, WeiQi Wei, Jonathan D. Mosley, C. Michael Stein, Vivian K. Kawai

Clinical Pharmacology and Therapeutics, vol. 111, 2022 Feb, pp. 435--443

Natural language processing to identify lupus nephritis phenotype in electronic health records

Yu Deng, Jennifer A. Pacheco, Anh Chung, Chengsheng Mao, Joshua C. Smith, Juan Zhao, Wei-Qi Wei, April Barnado, Chunhua Weng, Cong Liu, Adam Cordon, Jingzhi Yu, Yacob Tedla, Abel Kho, Rosalind Ramsey-Goldman, Theresa Walunas, Yuan Luo

2021 Dec

American Heart Association Precision Medicine Platform Addresses Challenges in Data Sharing

Laura M. Stevens, James A. de Lemos, Sandeep R. Das, Christine Rutan, Heather M. Alger, Mitchell S.V. Elkind, Juan Zhao, Kritika Iyer, C. Alberto Figueroa, Jennifer L. Hall

Circulation: Cardiovascular Quality and Outcomes, vol. 14, 2021 Sep, pp. e007949

Machine Learning Challenges in Pharmacogenomic Research

Wei-Qi Wei, Juan Zhao, Dan M. Roden, Josh F. Peterson

Clinical Pharmacology and Therapeutics, vol. 110, 2021 Sep, pp. 552--554

View all


Using NLP on unstructured EHR for COVID-19 surveillance

We applied NLP pipeline and developed association analysis called ConceptWAS to track early symptoms for COVID-19

Machine learning using longitudinal EHR and genetic data to improve cardiovascular disease prediction

We developed machine learning and deep learning models to predict 10-year CVD risk using longitudinal EHR and genetic data.

Deep phenotyping using unsupervised machine learning

Applied a state-of-arts tensor factorization method to longitudinal EHR data


Juan Zhao

Contact description

Department of Biomedical Informatics

Vanderbilt University Medical Center


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