Journalartikel - Wissenschaftlicher Artikel | 2021 | Peer reviewed

Swarm Learning for Decentralized and Confidential Clinical Machine Learning

Autor*innen

Warnat-Herresthal, Stefanie ; Schultze, Hartmut ; Shastry, Krishnaprasad Lingadahalli ; Manamohan, Sathyanarayanan ; Mukherjee, Saikat ; Garg, Vishesh ; Sarveswara, Ravi ; Händler, Kristian ; Pickkers, Peter ; Aziz, N. Ahmad ; Ktena, Sofia ; Tran, Florian ; Bitzer, Michael ; Ossowski, Stephan ; Casadei, Nicolas ; Herr, Christian ; Petersheim, Daniel ; Behrends, Uta ; Kern, Fabian ; Fehlmann, Tobias ; Schommers, Philipp ; Lehmann, Clara ; Augustin, Max ; Rybniker, Jan ; Altmüller, Janine ; Mishra, Neha ; Bernardes, Joana P. ; Krämer, Benjamin ; Bonaguro, Lorenzo ; Schulte-Schrepping, Jonas ; De Domenico, Elena ; Siever, Christian ; Kraut, Michael ; Desai, Milind ; Monnet, Bruno ; Saridaki, Maria ; Siegel, Charles Martin ; Drews, Anna ; Nuesch-Germano, Melanie ; Theis, Heidi ; Heyckendorf, Jan ; Schreiber, Stefan ; Kim-Hellmuth, Sarah ; Balfanz, Paul ; Eggermann, Thomas ; Boor, Peter ; Hausmann, Ralf ; Kuhn, Hannah ; Isfort, Susanne ; Stingl, Julia Carolin ; Schmalzing, Günther ; Kuhl, Christiane K. ; Röhrig, Rainer ; Marx, Gernot ; Uhlig, Stefan ; Dahl, Edgar ; Müller-Wieland, Dirk ; Dreher, Michael ; Marx, Nikolaus ; Nattermann, Jacob ; Skowasch, Dirk ; Kurth, Ingo ; Keller, Andreas ; Bals, Robert ; Nürnberg, Peter ; Rieß, Olaf ; Rosenstiel, Philip ; Netea, Mihai G. ; Theis, Fabian ; Mukherjee, Sach ; Backes, Michael ; Aschenbrenner, Anna C. ; Ulas, Thomas ; Angelov, Angel ; Bartholomäus, Alexander ; Becker, Anke ; Bezdan, Daniela ; Blumert, Conny ; Bonifacio, Ezio ; Bork, Peer ; Boyke, Bunk ; Blum, Helmut ; Clavel, Thomas ; Colome-Tatche, Maria ; Cornberg, Markus ; De La Rosa Velázquez, Inti Alberto ; Diefenbach, Andreas ; Dilthey, Alexander ; Fischer, Nicole ; Förstner, Konrad ; Franzenburg, Sören ; Frick, Julia-Stefanie ; Gabernet, Gisela ; Gagneur, Julien ; Ganzenmueller, Tina ; Gauder, Marie ; Geißert, Janina ; Goesmann, Alexander ; Göpel, Siri ; Grundhoff, Adam ; Grundmann, Hajo ; Hain, Torsten ; Hanses, Frank ; Hehr, Ute ; Heimbach, André ; Hoeper, Marius ; Horn, Friedemann ; Hübschmann, Daniel ; Hummel, Michael ; Iftner, Thomas ; Iftner, Angelika ; Illig, Thomas ; Janssen, Stefan ; Kalinowski, Jörn ; Kallies, René ; Kehr, Birte ; Keppler, Oliver T. ; Klein, Christoph ; Knop, Michael ; Kohlbacher, Oliver ; Köhrer, Karl ; Korbel, Jan ; Kremsner, Peter G. ; Kühnert, Denise ; Landthaler, Markus ; Li, Yang ; Ludwig, Kerstin U. ; Makarewicz, Oliwia ; Marz, Manja ; McHardy, Alice C. ; Mertes, Christian ; Münchhoff, Maximilian ; Nahnsen, Sven ; Nöthen, Markus ; Ntoumi, Francine ; Overmann, Jörg ; Peter, Silke ; Pfeffer, Klaus ; Pink, Isabell ; Poetsch, Anna R. ; Protzer, Ulrike ; Pühler, Alfred ; Rajewsky, Nikolaus ; Ralser, Markus ; Reiche, Kristin ; Ripke, Stephan ; da Rocha, Ulisses Nunes ; Saliba, Antoine-Emmanuel ; Sander, Leif Erik ; Sawitzki, Birgit ; Scheithauer, Simone ; Schiffer, Philipp ; Schmid-Burgk, Jonathan ; Schneider, Wulf ; Schulte, Eva-Christina ; Sczyrba, Alexander ; Sharaf, Mariam L. ; Singh, Yogesh ; Sonnabend, Michael ; Stegle, Oliver ; Stoye, Jens ; Vehreschild, Janne ; Velavan, Thirumalaisamy P. ; Vogel, Jörg ; Volland, Sonja ; von Kleist, Max ; Walker, Andreas ; Walter, Jörn ; Wieczorek, Dagmar ; Winkler, Sylke ; Ziebuhr, John ; Breteler, Monique M. B. ; Giamarellos-Bourboulis, Evangelos J. ; Kox, Matthijs ; Becker, Matthias ; Cheran, Sorin ; Woodacre, Michael S. ; Goh, Eng Lim ; Schultze, Joachim L.

Zeitschrift

Nature

DOI

10.1038/s41586-021-03583-3

URL

https://www.nature.com/articles/s41586-021-03583-3.pdf

Weitere Details

Sprache

englisch

Darstellungsform

Text

Band

594

Ausgabe

7862

Seitenbereich

265 - 270

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. 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.

Zitierstile

Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., … Schultze, J. L. (2021). Swarm Learning for Decentralized and Confidential Clinical Machine Learning. Nature, 594(7862), 265–270. doi:10.1038/s41586-021-03583-3

In die Zwischenablage kopieren

Warnat-Herresthal, S., Schultze, H., Shastry, K.L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N.A., Ktena, S., Tran, F., Bitzer, M., Ossowski, S., Casadei, N., Herr, C., Petersheim, D., Behrends, U., Kern, F., Fehlmann, T., Schommers, P., Lehmann, C., Augustin, M., Rybniker, J., Altmüller, J., Mishra, N., Bernardes, J.P., Krämer, B., Bonaguro, L., Schulte-Schrepping, J., De Domenico, E., Siever, C., Kraut, M., Desai, M., Monnet, B., Saridaki, M., Siegel, C.M., Drews, A., Nuesch-Germano, M., Theis, H., Heyckendorf, J., Schreiber, S., Kim-Hellmuth, S., Balfanz, P., Eggermann, T., Boor, P., Hausmann, R., Kuhn, H., Isfort, S., Stingl, J.C., Schmalzing, G., Kuhl, C.K., Röhrig, R., Marx, G., Uhlig, S., Dahl, E., Müller-Wieland, D., Dreher, M., Marx, N., Nattermann, J., Skowasch, D., Kurth, I., Keller, A., Bals, R., Nürnberg, P., Rieß, O., Rosenstiel, P., Netea, M.G., Theis, F., Mukherjee, S., Backes, M., Aschenbrenner, A.C., Ulas, T., Angelov, A., Bartholomäus, A., Becker, A., Bezdan, D., Blumert, C., Bonifacio, E., Bork, P., Boyke, B., Blum, H., Clavel, T., Colome-Tatche, M., Cornberg, M., De La Rosa Velázquez, I.A., Diefenbach, A., Dilthey, A., Fischer, N., Förstner, K., Franzenburg, S., Frick, J.-S., Gabernet, G., Gagneur, J., Ganzenmueller, T., Gauder, M., Geißert, J., Goesmann, A., Göpel, S., Grundhoff, A., Grundmann, H., Hain, T., Hanses, F., Hehr, U., Heimbach, A., Hoeper, M., Horn, F., Hübschmann, D., Hummel, M., Iftner, T., Iftner, A., Illig, T., Janssen, S., Kalinowski, J., Kallies, R., Kehr, B., Keppler, O.T., Klein, C., Knop, M., Kohlbacher, O., Köhrer, K., Korbel, J., Kremsner, P.G., Kühnert, D., Landthaler, M., Li, Y., Ludwig, K.U., Makarewicz, O., Marz, M., McHardy, A.C., Mertes, C., Münchhoff, M., Nahnsen, S., Nöthen, M., Ntoumi, F., Overmann, J., Peter, S., Pfeffer, K., Pink, I., Poetsch, A.R., Protzer, U., Pühler, A., Rajewsky, N., Ralser, M., Reiche, K., Ripke, S., da Rocha, U.N., Saliba, A.-E., Sander, L.E., Sawitzki, B., Scheithauer, S., Schiffer, P., Schmid-Burgk, J., Schneider, W., Schulte, E.-C., Sczyrba, A., Sharaf, M.L., Singh, Y., Sonnabend, M., Stegle, O., Stoye, J., Vehreschild, J., Velavan, T.P., Vogel, J., Volland, S., von Kleist, M., Walker, A., Walter, J., Wieczorek, D., Winkler, S., Ziebuhr, J., Breteler, M.M.B., Giamarellos-Bourboulis, E.J., Kox, M., Becker, M., Cheran, S., Woodacre, M.S., Goh, E.L. and Schultze, J.L., 2021. Swarm Learning for Decentralized and Confidential Clinical Machine Learning. Nature, [online] 594(7862), pp.265–270. Available at: <https://www.nature.com/articles/s41586-021-03583-3.pdf>.

In die Zwischenablage kopieren

Warnat-Herresthal, Stefanie u. a. 2021. Swarm Learning for Decentralized and Confidential Clinical Machine Learning. Nature 594, 7862, 265–270. https://www.nature.com/articles/s41586-021-03583-3.pdf.

American Psychological Association 6th edition (DOI)

Anglia Ruskin University - Harvard

Gesellschaft für Bildung und Forschung in Europa - Harvard