CONFEDERATED MACHINE LEARNING ON HORIZONTALLY AND VERTICALLY SEPARATED MEDICAL DATA FOR LARGE-SCALE HEALTH SYSTEM INTELLIGENCE

Dianbo Liu, Tim Miller, Kenneth Mandl

Keywords: Confederated learning, siloed medical data, representation joining

TL;DR: a confederated learning method that train model from horizontally and vertically separated medical data

Abstract:

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 given
patient 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 machine
learning model to stratify the risk of accidental falls among the elderly.