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Health information is generally fragmented across silos. Though it istechnically feasible to unite data for analysis in a manner that underpins arapid learning healthcare system, privacy concerns and regulatory barrierslimit data centralization. Machine learning can be conducted in a federatedmanner on patient datasets with the same set of variables, but separated acrosssites of care. But federated learning cannot handle the situation wheredifferent data types for a given patient are separated vertically acrossdifferent organizations and when patient ID matching across differentinstitutions is difficult. We call methods that enable machine learning modeltraining on data separated by two or more degrees confederated machinelearning. We proposed and evaluated a confederated learning to training machinelearning model to stratify the risk of several diseases among when data arehorizontally separated by individual, vertically separated by data type, andseparated by identity without patient ID matching.

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