Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
By Dianbo Liu, Timothy Miller, Kenneth D. Mandl. 2019
10 citations recorded by the source corpus.
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. We call methods that enable machine learning model training on data separated by two or more degrees confederated machine learning. We proposed and evaluated a confederated learning to training machine learning model to stratify the risk of several diseases among when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching.
Subfields: Artificial Intelligence, Information Systems and Management
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dianbo Liu
- Arne Küderle
- Rodolfo Stoffel Antunes
- Cristiano André da Costa
- B. Eskofier
- Imrana Abdullahi Yari
- Li Huang
- Yifeng Yin
In Information Systems and Management
Citations in the corpus
- LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data (2018, methodology): “used a federated transfer learning strategy to balance global and local learning [13–16,20].”
- Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, methodology): “[93] employ a GAN to generate the missing data on local databases before use on an ML algorithm.”
- Federated Learning for Smart Healthcare: A Survey (2021, methodology): “[95] HFL Medical sites Data centre An FL-based approach for large nationwide health insurance data analytics in the US.”