Objective Accurate and rapid phenoty** is a prerequisite to leveraging electronic health
records for biomedical research. While early phenoty** relied on rule-based algorithms …

Artificial intelligence (AI) and machine learning (ML) research within medicine has been
exponentially increasing over the last decade, with studies showcasing the potential of …

Synthesizing information from multiple data sources is critical to ensure knowledge
generalizability. Integrative analysis of multi-source data is challenging due to the …

Objectives To characterize the complex interplay between multiple clinical conditions in a
time-to-event analysis framework using data from multiple hospitals, we developed two …

Abstract Objective We propose FedScore, a privacy-preserving federated learning
framework for scoring system generation across multiple sites to facilitate cross-institutional …

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

For healthcare datasets, it is often impossible to combine data samples from multiple sites
due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of …

ABSTRACT A retrospective observational study is one of the most widely used research
methods in medicine. However, evidence postulated from a single data source likely …

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 …