Eils Group, Hub for Innovations in Digital Health, Service center: Heidelberg Center for Human Bioinformatics – HD-HuB
Machine learning methods hold the promise of great benefits for patients, physicians and researchers but require vast amounts of data. While this data typically exists in large research hospitals, it is generally inaccessible due to legal, ethical and privacy concerns. By building a secure computing framework, following the model-to-data approach, we aim at opening medical data for research purposes, without compromising security.
The core idea is that the sensitive data stays within the hospital’s servers, pseudonymized and protected by existing safeguards. Researchers will work with the data by sending in their code for machine learning models, which will then be executed on the data using on-site high-performance computing resources. While performance metrics are generally sent back to the researchers to allow for code changes and model improvements, models will only be sent back after thorough privacy checks.
In this framework, the hosting hospital stays in full control over their (patient) data and does not disclose personalized or otherwise sensitive data to researchers while still enabling research for a wide scientific community.
For further information, please visit ails lab.
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