PDA: privacy-preserving distributed algorithms and statistical inference

Seminar Series

Monday, April 26, 2021 - 12:15
Zoom Conference
Yong Chen, PhD

Abstract: 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 IPD, we developed a toolbox of Privacy-preserving Distributed Algorithms (PDA) that conduct distributed learning and inference for various models, such as logistic regression, Cox model, Poisson model, and more. Our algorithms do not require iterative communication across sites and are able to account for heterogeneity across different hospitals. In addition, PDA outperforms meta-analysis methods in many settings such as pharmacovigilance applications.  The validity and efficiency of PDA are also demonstrated with real-world use cases in Penn Medicine Biobank (PMBB), Observational Health Data Sciences and Informatics (OHDSI) and a Pediatric Learning Health System (PEDSnet).

Speaker: Yong Chen, PhD
Associate Professor of Biostatistics
Department of Biostatistics, Epidemiology & Bioinformatics
Perelman School of Medicine
University of Pennsylvania

Dr. Yong Chen is an Associate Professor of Biostatistics at the Department of Biostatistics, Epidemiology and Informatics (DBEI), the Perelman School of Medicine, University of Pennsylvania (Penn). He is the PI of the PennCIL lab, a computing, inference and learning lab at University of Pennsylvania, which is aiming to tackle key challenges in the modern data rich era, including heterogeneity, complexity, suboptimal quality, reproducibility, privacy, and high-dimensionality of biomedical data. He has published over 100 papers. His research has been continuously funded by NIH, PCORI and AHRQ.

Zoom: https://duke.zoom.us/j/93580095535?pwd=VkYyL0NUejdXdEhxUmJnN2dsQjB6QT09  

Passcode: 537879