Bringing the Algorithms to the Data -- Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)

• DOI: 10.48550/arxiv.2212.03481
• Publication Date: 2022-01-01
• Authors: Herr, Marius de A...; Graf, Michael; Placzek, Peter; König, Florian; Bötte, Felix; Stickel, Tyra; Hieber, David; Zimmermann, Lukas; Slupina, Michael; Mohr, Christopher; Biergans, Stephanie; Akgün, Mete; Pfeifer, Nico; Kohlbacher, Oliver
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computers and Society; 0303 health sciences; 03 medical and health sciences; Computer Science - Cryptography and Security; Computer Science - Distributed, Parallel, and Cluster Computing; Computers and Society (cs.CY); Distributed, Parallel, and Cluster Computing (cs.DC); Cryptography and Security (cs.CR); 3. Good health; Machine Learning (cs.LG)

The need for data privacy and security -- enforced through increasingly strict data protection regulations -- renders the use of healthcare data for machine learning difficult. In particular, the transfer of data between different hospitals is often not permissible and thus cross-site pooling of data not an option. The Personal Health Train (PHT) paradigm proposed within the GO-FAIR initiative implements an 'algorithm to the data' paradigm that ensures that distributed data can be accessed for analysis without transferring any sensitive data. We present PHT-meDIC, a productively deployed open-source implementation of the PHT concept. Containerization allows us to easily deploy even complex data analysis pipelines (e.g, genomics, image analysis) across multiple sites in a secure and scalable manner. We discuss the underlying technological concepts, security models, and governance processes. The implementation has been successfully applied to distributed analyses of large-scale data, including applications of deep neural networks to medical image data.