Di-Yang Xue(薛递洋) Ph.D. student, Intelligent Systems Program School of Computing and Information, University of Pittsburgh. Email: dix2 at pitt dot edu

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News

• 2017 Fall term. Serve as teaching assistant for BIOINF 2011-Foundations of Clinical and Public Health Informatics.
• 05/26/2017. Our paper "Predictive Modeling for Classification of Positive Valence System Symptom Severity from Initial Psychiatric Evaluation Records" is accepted by Journal of Biomedical Informatics.
• 11/21/2016. Passed comprehensive exam.
• 04/07/2015. Passed preliminary exam.

Research Interests:

• Artificial Intelligence in Medicine

Education:

• PhD student in Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 2014-present.
• MSc in Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 2012-2015.
• MSc in Biostatistics, Peking university Health Science Center, Bei Jing, China, 2006- 2009
• BSc in Preventive Medicine, Shan Dong University, Ji Nan, China, 2001-2006

Publications:

• Tsui F, Shi L, Ruiz V, Barda A, Ye Y, Xue D, Mi F, Jain U.Detection of adverse drug reaction from twitter data.CEUR Workshop Proceedings.2017 vol: 1996[link]
• Posada JD, Barda AJ, Shi L, Xue D, Ruiz V, Kuan PH, Ryan ND, Rich TF. Predictive Modeling for Classification of Positive Valence System Symptom Severity from Initial Psychiatric Evaluation Records. Journal of biomedical informatics. 2017 May 29.[link]
• Jiang X, Cai B, Xue D, Lu X, Cooper GF, Neapolitan RE. A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets. Journal of the American Medical Informatics Association. 2014 Sep 16.[link]
• Neapolitan R, Xue D, Jiang X. Modeling the altered expression levels of genes on signaling pathways in tumors as causal Bayesian networks. Cancer informatics. 2014 May 25.[link]
• Jiang X, Xue D, Brufsky A, Khan S, Neapolitan R. A new method for predicting patient survivorship using efficient Bayesian network learning. Cancer informatics. 2014 Feb 13.[link]

Talks:

• ISP Colloquium Series, 02/03/2017. Heart Failure Readmission Risk Assessment Using Electronic Health Records from 15,703 Hospital Visits

Awards:

• Distinguished Poster Award, American Medical Informatics Association Conference,2015. Victor M. Ruiz, Ye Ye, Amie J. Draper, Diyang Xue, Jose Posada, Ancrew H. Urbach, and Fuchiang Tsui. Use of diagnosis-related groups to predict all-cause pediatric hospital readmission within 30 days after discharge. 2015 AMIA, San Francisco, CA.