Ye Ye, PhD
- Post Doctoral Associate, Department of Biomedical Informatics
Dr. Ye received her PhD in Intelligent System Program from University of Pittsburgh, MS in Public Health Informatics (invited into the Delta Omega Honor Society in Public Health) from Emory University, and she obtained medical training, Epidemiology, and Health Statistics from Peking University, China.
Her previous research mainly focused on predictive modeling techniques and natural language processing, which she has applied to infectious disease case detection (two first-author and three second-author journal articles), outbreak detection (two journal articles), patient readmission risk profiling (two second-author conference papers), disease surveillance using nationwide over-the-counter products (one coauthored journal article and one conference paper), and population adverse drug reaction monitoring (one coauthored conference paper).
Recently, Dr. Ye received an NLM K99 grant, with which she is developing transfer learning algorithms to improve the re-usability of computable biomedical knowledge for infectious disease case detection and pathology image analysis.
Representative Publications
- Ye Y, Wagner MM, Cooper GF, Ferraro JP, Su H, Gesteland PH, Haug PJ, Millett NE, Aronis JM, Nowalk AJ, Ruiz VM. A study of the transferability of influenza case detection systems between two large healthcare systems. PLoS One. 2017 Apr 5;12(4):e0174970.
- Ye Y, Tsui F, Wagner M, Espino JU, Li Q. Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers. Journal of the American Medical Informatics Association. 2014 Jan 9;21(5):815-23.
- Ferraro JP, Ye Y, Gesteland PH, Haug PJ, Tsui F, Cooper GF, Van Bree R, Ginter T, Nowalk AJ, Wagner MM. The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance. Applied Clinical Informatics. 2017 Feb;8(02):560-80.
- Lu S, Ye Y, Tsui R, Liu X, Hwa R. Feature selection for 30-day heart failure readmission prediction using clinical drug data. NIPS Workshop on Machine Learning for Clinical Data Analysis and Healthcare, Harrahs and Harveys, Lake Tahoe, 2013.
- Tang L, Lyles RH, Ye Y, Lo Y, King CC. Extended matrix and inverse matrix methods utilizing internal validation data when both disease and exposure status are misclassified. Epidemiologic methods. 2013 Sep 1;2(1):49-66. *Awarded Best Statistical Science Theoretical Paper from Centers for Disease Control and Prevention in 2014.
Research Interests
- Transfer Learning
- Population Health Surveillance
- Computer Vision In Pathology Imaging
- Automated Predictive Modeling
- Evaluation of medical informatics system