Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence

Understand

Health information is generally fragmented across silos.

• Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization.
• Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care.
• But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institutions is difficult.