Biostatistics Weekly Seminar

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).
Learning objectives- Objective 1: pragmatic considerations and principles in running federated learning models in distributed research networks (DRNs) Objective 2: methodologies to address various types of heterogeneities Objective 3: integration of federated learning with downstream tasks such as counterfactual modeling and causal inference

2525 West End Ave, 15th floor
Conference Room 1561
Zoom Link to Follow
18 October 2023
1:30pm