Unknown

Dataset Information

0

Swarm Learning for decentralized and confidential clinical machine learning.


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.

SUBMITTER: Warnat-Herresthal S 

PROVIDER: S-EPMC8189907 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

altmetric image

Publications

Swarm Learning for decentralized and confidential clinical machine learning.

Warnat-Herresthal Stefanie S   Schultze Hartmut H   Shastry Krishnaprasad Lingadahalli KL   Manamohan Sathyanarayanan S   Mukherjee Saikat S   Garg Vishesh V   Sarveswara Ravi R   Händler Kristian K   Pickkers Peter P   Aziz N Ahmad NA   Ktena Sofia S   Tran Florian F   Bitzer Michael M   Ossowski Stephan S   Casadei Nicolas N   Herr Christian C   Petersheim Daniel D   Behrends Uta U   Kern Fabian F   Fehlmann Tobias T   Schommers Philipp P   Lehmann Clara C   Augustin Max M   Rybniker Jan J   Altmüller Janine J   Mishra Neha N   Mishra Neha N   Bernardes Joana P JP   Krämer Benjamin B   Bonaguro Lorenzo L   Schulte-Schrepping Jonas J   De Domenico Elena E   Siever Christian C   Kraut Michael M   Desai Milind M   Monnet Bruno B   Saridaki Maria M   Siegel Charles Martin CM   Drews Anna A   Nuesch-Germano Melanie M   Theis Heidi H   Heyckendorf Jan J   Schreiber Stefan S   Kim-Hellmuth Sarah S   Nattermann Jacob J   Skowasch Dirk D   Kurth Ingo I   Keller Andreas A   Bals Robert R   Nürnberg Peter P   Rieß Olaf O   Rosenstiel Philip P   Netea Mihai G MG   Theis Fabian F   Mukherjee Sach S   Backes Michael M   Aschenbrenner Anna C AC   Ulas Thomas T   Breteler Monique M B MMB   Giamarellos-Bourboulis Evangelos J EJ   Kox Matthijs M   Becker Matthias M   Cheran Sorin S   Woodacre Michael S MS   Goh Eng Lim EL   Schultze Joachim L JL  

Nature 20210526 7862


Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine<sup>1,2</sup>. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes<sup>3</sup>. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation<sup>4,5</sup>. Here, to facilitate the integration of any medical data from any data owner worldwide without violating  ...[more]

Similar Datasets

| S-EPMC9919887 | biostudies-literature
| S-EPMC4849747 | biostudies-literature
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
| S-EPMC10017272 | biostudies-literature
| S-EPMC6689279 | biostudies-other
| S-EPMC10851139 | biostudies-literature
| S-EPMC8786279 | biostudies-literature
| S-EPMC7157505 | biostudies-literature
| S-EPMC11302612 | biostudies-literature
| S-EPMC8435025 | biostudies-literature