Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
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.
Cite
Text
Liu et al. "Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence." International Conference on Learning Representations, 2020.Markdown
[Liu et al. "Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence." International Conference on Learning Representations, 2020.](https://mlanthology.org/iclr/2020/liu2020iclr-confederated/)BibTeX
@inproceedings{liu2020iclr-confederated,
title = {{Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence}},
author = {Liu, Dianbo and Miller, Timothy A and Mandl, Kenneth D.},
booktitle = {International Conference on Learning Representations},
year = {2020},
url = {https://mlanthology.org/iclr/2020/liu2020iclr-confederated/}
}