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

• URL: http://arxiv.org/abs/2212.03481v1
• Date: Wed, 7 Dec 2022 06:29:15 GMT
• Title: Bringing the Algorithms to the Data -- Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)
• Authors: Marius de Arruda Botelho Herr, Michael Graf, Peter Placzek, Florian K\"onig, Felix B\"otte, Tyra Stickel, David Hieber, Lukas Zimmermann, Michael Slupina, Christopher Mohr, Stephanie Biergans, Mete Akg\"un, Nico Pfeifer, Oliver Kohlbacher
• Abstract summary: Personal Health Train (PHT) paradigm implements an 'algorithm to the data' paradigm. We present PHT-meDIC, a productively deployed open-source implementation of the PHT concept.
• Score: 1.451998131020241
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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