Bringing the Algorithms to the Data -- Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)
This addresses the problem of data privacy in healthcare analytics for researchers and institutions, enabling cross-site collaboration without violating regulations, though it is incremental as it builds on the existing PHT concept.
The paper tackles the challenge of using healthcare data for machine learning under strict privacy regulations by introducing PHT-meDIC, an open-source implementation of the Personal Health Train paradigm that enables secure distributed analytics without transferring sensitive data, and it has been successfully deployed for applications like deep neural networks on medical images.
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.