Yong Chenمشاهده پروفایل
استادیار
- statistical methods for electronic health record data
- meta-analysis
- mathematical statistics
- +۳ مورد دیگر
Yong Chen, PhD, is an Assistant Professor of Biostatistics specializing in advanced statistical methodologies for healthcare data analysis. His research bridges biostatistics, machine learning, and real-world evidence generation with significant contributions to federated learning frameworks and meta-analytic techniques. His core research interests include: Statistical methods for electronic health record data Meta-analysis and network meta-analysis Mathematical statistics with healthcare applications Federated learning for decentralized research networks Causal inference methods for observational data Real-world evidence generation in rare diseases Analysis of Dr. Chen's 2025 publications reveals a dominant focus on privacy-preserving distributed analytics for multi-site healthcare studies, particularly through one-shot lossless algorithms (e.g., COLA-GLM). His work demonstrates methodological innovation in addressing unmeasured confounding through negative control calibration and advancing multitask learning frameworks. Key application areas include SARS-CoV-2 sequelae analysis, neuropsychiatric outcomes, and disparities in obesity treatment access, consistently leveraging electronic health records for real-world evidence generation. Scientific recognition: FACMI (Fellow of the American College of Medical Informatics)






