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Paving the way for machine learning with medical data

Press releases may be edited for formatting or style | July 28, 2021 Artificial Intelligence
The Center for Advanced Systems Understanding (CASUS) at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) has joined PIONEER, a 12.8m euro project funded by the public-private partnership Innovative Medicines Initiative 2 (IMI2). The HZDR is PIONEER's 36th member. The European consortium aims to transform the field of prostate cancer care by unlocking the potential of big data and big data analytics. Spread all across Europe, databases from clinical studies, public registries and electronic health records contain clinical data from thousands of prostate cancer patients. PIONEER collects, anonymizes and assembles these diverse data sets. CASUS takes over the task of providing a new centralized data and analytics platform for PIONEER. The cloud-based platform will provide data access and machine learning analytics capabilities for both academia and industry researchers. PIONEER operates both a central and federated model of data sharing. For the federated model, CASUS will take on the challenge of establishing a federated analytics network. The use of both data sharing models has allowed PIONEER to maximize both data protection and data utilization.

Prof. James N’Dow, Academic lead for PIONEER and Adjunct Secretary General of the European Association of Urology, welcomes the new partner HZDR to the consortium: “The expertise of HZDR’s CASUS in large-scale data management will provide a secure, scalable and sustainable infrastructure to host the PIONEER Prostate Cancer Big Data Platform. We are excited to embark on this next stage of PIONEER with CASUS.” Besides providing the PIONEER Big Data Platform cloud infrastructure, CASUS will also set up and support federated data analysis for all members of the consortium. For Dr. Michael Bussmann, Scientific Head of the Görlitz (Germany)-based research center, this aspect is of paramount importance: “By developing advanced machine learning algorithms, we expect to come up with better predictive models of patient outcomes and disease progression. The focus is on established and new clinical and biological indicators, so-called biomarkers. We will try to find out if and how recording such biomarkers improves predictions throughout a prostate cancer patient’s care pathway.”

Safeguarding data access and data protection

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Many stakeholders throughout the healthcare system collect medical data. Data-driven machine learning is considered a powerful tool for analyzing these data. To achieve this, however, data must all be available in a common format and all data protection concerns must be adequately addressed.

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