小柯机器人

德国波恩大学Joachim L. Schultze团队开发出用于分散且保密的临床机器学习新方法。相关论文于2021年5月26日在线发表在《自然》杂志上。

研究人员表示，快速可靠地检测患有严重和异质性疾病的患者是精准医学的主要目标。白血病患者可以根据血液转录组使用机器学习来鉴别。然而，由于隐私立法，技术上可行和法律允许之间的分歧越来越大。

为了在不违反隐私法的情况下促进整合来自全球任何数据所有者的任何医疗数据，研究人员引入了Swarm Learning——一种分散式机器学习方法，将边缘计算、基于区块链的点对点网络和协调结合起来，同时保持机密性无需中央协调器，从而超越联邦学习。为了说明使用Swarm Learning使用分布式数据开发疾病分类器的可行性，研究人员选择了异类疾病的四个用例（COVID-19、结核病、白血病和肺部病变）。

凭借来自127项临床研究的16,400多个血液转录组、病例和对照的分布不均匀以及大量研究偏差，以及 95,000 多张胸部X射线图像，研究人员表明，Swarm Learning分类器的性能优于在单个站点开发的分类器。此外，Swarm Learning在设计上完全符合当地的保密规定。研究人员认为这种方法将显著加速精准医疗的引入。

附：英文原文

Title: Swarm Learning for decentralized and confidential clinical machine learning

Author: Stefanie Warnat-Herresthal, Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Hndler, Peter Pickkers, N. Ahmad Aziz, Sofia Ktena, Florian Tran, Michael Bitzer, Stephan Ossowski, Nicolas Casadei, Christian Herr, Daniel Petersheim, Uta Behrends, Fabian Kern, Tobias Fehlmann, Philipp Schommers, Clara Lehmann, Max Augustin, Jan Rybniker, Janine Altmller, Neha Mishra, Joana P. Bernardes, Benjamin Krmer, Lorenzo Bonaguro, Jonas Schulte-Schrepping, Elena De Domenico, Christian Siever, Michael Kraut, Milind Desai, Bruno Monnet, Maria Saridaki, Charles Martin Siegel, Anna Drews, Melanie Nuesch-Germano, Heidi Theis, Jan Heyckendorf, Stefan Schreiber, Sarah Kim-Hellmuth, Jacob Nattermann, Dirk Skowasch, Ingo Kurth, Andreas Keller, Robert Bals, Peter Nrnberg, Olaf Rie, Philip Rosenstiel, Mihai G. Netea, Fabian Theis, Sach Mukherjee, Michael Backes, Anna C. Aschenbrenner, Thomas Ulas, Monique M. B. Breteler, Evangelos J. Giamarellos-Bourboulis, Matthijs Kox, Matthias Becker

Issue&Volume: 2021-05-26

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.

DOI: 10.1038/s41586-021-03583-3

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

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html