Seminar by Prof. Yong CHEN from Department of Biostatistics, University of Pennsylvania

With the increasing availability of electronic health records (EHR) data, it is important to effectively integrate evidence from multiple data sources to enable reproducible scientific discovery. However, we are still facing practical challenges in data integration, such as protection of data privacy, the high dimensionality of features, and heterogeneity across different datasets. Aim to facilitate efficient multi-institutional data analysis without sharing individual patient data (IPD), we developed a toolbox of Privacy-preserving Distributed Algorithms (PDA) that conduct distributed learning and inference for various models, such as association analyses, causal inference, cluster analyses, counterfactual analyses, and beyond. Our algorithms do not require iterative communication across sites and are able to account for heterogeneity across different hospitals. The validity and efficiency of PDA are also demonstrated with real-world use cases in Observational Health Data Sciences and Informatics (OHDSI), PCORnets including PEDSnet and OneFlorida, and Penn Medicine Biobank (PMBB).

Prof. Yong Chen is tenured Professor of Biostatistics and the Founding Director of the Center for Health AI and Synthesis of Evidence (CHASE) at the University of Pennsylvania. He is an elected fellow of American Statistical Association, International Statistical Institute, Society for Research Synthesis Methodology, American College of Medical Informatics, and American Medical Informatics Association. He founded the Penn Computing, Inference and Learning (PennCIL) lab at the University of Pennsylvania, focusing on clinical evidence generation and evidence synthesis using clinical and real-world data. Prof. Chen has published over 200 peer-reviewed papers in statistical inference, medical informatics, comparative effectiveness research, and biomedical sciences, including 9 papers at Biometrika and JASA, and 13 papers at Biometrics and Annals of Applied Statistics.