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Swarm Learning for decentralized and confidential clinical machine learning
- Source :
- Nature, 594(7862), 265-270. Nature Publishing Group, Nature, Nature, 594, 7862, pp. 265-270, Nature, 594, 265-270, Nature (2021), Nature
594(7862), 265-270 (2021). doi:10.1038/s41586-021-03583-3 - Publication Year :
- 2021
-
Abstract
- Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.<br />Swarm Learning is a decentralized machine learning approach that outperforms classifiers developed at individual sites for COVID-19 and other diseases while preserving confidentiality and privacy.
- Subjects :
- Lung Diseases
Male
0301 basic medicine
Cancer Research
Computer science
lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4]
Privacy laws of the United States
Datasets as Topic
computer.software_genre
Disease Outbreaks
0302 clinical medicine
Software
diagnosis [Leukemia]
pathology [Leukemia]
Leukocytes
Computational models
Use case
Confidentiality
030212 general & internal medicine
Precision Medicine
Edge computing
Leukemia
Multidisciplinary
Swarm behaviour
diagnosis [Lung Diseases]
ddc
3. Good health
diagnosis [Tuberculosis]
Female
ddc:500
Clinical Decision-Making
Predictive medicine
methods [Clinical Decision-Making]
pathology [Leukocytes]
Machine learning
Article
03 medical and health sciences
methods [Precision Medicine]
Blockchain
Humans
Tuberculosis
trends [Machine Learning]
business.industry
COVID-19
Diagnostic markers
diagnosis [COVID-19]
epidemiology [COVID-19]
Precision medicine
030104 developmental biology
Cardiovascular and Metabolic Diseases
Viral infection
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 00280836
- Database :
- OpenAIRE
- Journal :
- Nature, 594(7862), 265-270. Nature Publishing Group, Nature, Nature, 594, 7862, pp. 265-270, Nature, 594, 265-270, Nature (2021), Nature <London> 594(7862), 265-270 (2021). doi:10.1038/s41586-021-03583-3
- Accession number :
- edsair.doi.dedup.....ec35177e99f77173021d0f7e89747def