PDA: Privacy-preserving Distributed Algorithms for Clinical Evidence Generation using Networked Data
The advent of digital healthcare records has ushered in a new era for medical research, offering unprecedented access to electronic health records (EHR) data. This surge in available data presents a unique opportunity to synthesize evidence from diverse sources, paving the way for significant scientific discoveries. Despite these advancements, the integration of such data is not without its challenges. Issues surrounding the protection of patient privacy, the complexity introduced by the vast array of data features, and the variability across different datasets pose significant hurdles. In response to these challenges, our team has developed an innovative suite of Privacy-preserving Distributed Algorithms (PDA). These tools are designed to facilitate comprehensive multi-institutional data analyses without the need to share individual patient data (IPD). Our PDA framework employs distributed learning and inference to support a variety of models, including association analyses, causal inference, cluster analyses, and counterfactual analyses, among others. This approach significantly contributes to the missions of data-centric ecosystems such as OHDSI, PCORnet, International Agency for Research on Cancer, and the RECOVER COVID Initiative. The practicality and effectiveness of our PDA framework are underscored by its successful application in a multitude of real-world scenarios, including pharmacoepidemiologic studies, the development of predictive models for early disease diagnosis, collaborative subphenotyping, and hospital performance evaluations.
Yong Chen is a Professor of Biostatistics and Founding Director of the Center for Health AI and Synthesis of Evidence (CHASE) at the University of Pennsylvania, where he leads research in clinical evidence generation and synthesis using real-world data. He also directs the Penn Computing, Inference, and Learning (PennCIL) lab, focusing on methods for integrating clinical data.
Dr. Chen serves as an Associate Editor for the Journal of the American Statistical Association (JASA) and The Annals of Applied Statistics (AoAS), a Statistical Consultant for New England Journal of Medicine-AI, and is a Commissioner on the Lancet Commission on Rare Disease.
During the pandemic, as Biostatistics Core Director for the RECOVER COVID Initiative, he led data studies on post-COVID conditions using information from over 9 million pediatric patients across 40 health systems. This work generated some of the first timely real-world evidence on the effectiveness and safety of COVID vaccines, as well as insights into the impacts of long COVID.
Dr. Chen has published over 200 peer-reviewed papers in statistics and medical informatics, with ongoing support from NIH, AHRQ, and PCORI. His research focuses on evidence synthesis, data integration, and precision medicine applications. He is also a fellow of the American Statistical Association and the American College of Medical Informatics, with joint appointments in Applied Mathematics and at the Penn Institute for Biomedical Informatics.
A reception will take place in the Jolley Hall fifth floor lobby after this event.