CONFEDERATED MACHINE LEARNING ON HORIZONTALLY AND VERTICALLY SEPARATED MEDICAL DATA FOR LARGE-SCALE HEALTH SYSTEM INTELLIGENCE
A patient’s 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 givenpatient are separated vertically across different organizations.We call methods that enable machine learning model training on data separated by two or more degrees “confederated machine learning.”We built and evaluated a confederated machinelearningmodel to stratify the risk of accidental falls among the elderly.
a confederated learning method that train model from horizontally and vertically separated medical data A "confederated" machine learning method that learns across divides in medical data separated both horizontally and vertically.