Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis.

ABSTRACT:

Background

There is an urgent need for the development of global analytic frameworks that can perform analyses in a privacy-preserving federated environment across multiple institutions without privacy leakage. A few studies on the topic of federated medical analysis have been conducted recently with the focus on several algorithms. However, none of them have solved similar patient matching, which is useful for applications such as cohort construction for cross-institution observational studies, disease surveillance, and clinical trials recruitment.

Objective

The aim of this study was to present a privacy-preserving platform in a federated setting for patient similarity learning across institutions. Without sharing patient-level information, our model can find similar patien

SUBMITTER: Lee J 

PROVIDER: S-EPMC5924379 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature