Objectives Federated learning (FL) has gained popularity in clinical research in recent years
to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent …

The progress of precision medicine research hinges on the gathering and analysis of
extensive and diverse clinical datasets. With the continued expansion of modalities, scales …

Integrating real-world data (RWD) from several clinical sites offers great opportunities to
improve estimation with a more general population compared to analyses based on a single …

Multi-task learning has emerged as a powerful machine learning paradigm for integrating
data from multiple sources, leveraging similarities between tasks to improve overall model …

Integrating data across institutions can improve learning efficiency. To integrate data
efficiently while protecting privacy, we propose A one-shot, summary-statistics-based, D …

Objective To develop a lossless distributed algorithm for generalized linear mixed model
(GLMM) with application to privacy-preserving hospital profiling. Materials and Methods The …

Objective To characterize the interplay between multiple medical conditions across sites and
account for the heterogeneity in patient population characteristics across sites within a …

The health insurance system in South Korea is known as the National Health Insurance
(NHI) system. It is a mandatory insurance system that covers approximately 97% of Korean …

Analyses of distributed data networks of rare diseases are constrained by legitimate privacy
and ethical concerns. Analytical centers (eg research institutions) are thus confronted with …

Objectives Racial disparities in kidney transplant access and posttransplant outcomes exist
between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in the United …