| Autor: |
Sun C; Institute of Data Science, Maastricht University, Maastricht, The Netherlands., Ippel L; Institute of Data Science, Maastricht University, Maastricht, The Netherlands., van Soest J; Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University, Medical Centre+, Maastricht, The Netherlands., Wouters B; Institute of Data Science, Maastricht University, Maastricht, The Netherlands., Malic A; Institute of Data Science, Maastricht University, Maastricht, The Netherlands., Adekunle O; Institute of Data Science, Maastricht University, Maastricht, The Netherlands., van den Berg B; Statistics Netherlands (Centraal Bureau voor de Statistiek), Heerlen, The Netherlands., Mussmann O; Statistics Netherlands (Centraal Bureau voor de Statistiek), Heerlen, The Netherlands., Koster A; Department of Social Medicine, CAPHRI Care and Public Health Research Institute, Maastricht University, The Netherlands., van der Kallen C; Department of Internal Medicine, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands., van Oppen C; Institute of Data Science, Maastricht University, Maastricht, The Netherlands., Townend D; Department of Health, Ethics and Society, CAPHRI Research School, Maastricht University, Maastricht, The Netherlands., Dekker A; Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University, Medical Centre+, Maastricht, The Netherlands., Dumontier M; Institute of Data Science, Maastricht University, Maastricht, The Netherlands. |
| Abstrakt: |
It is widely anticipated that the use and analysis of health-related big data will enable further understanding and improvements in human health and wellbeing. Here, we propose an innovative infrastructure, which supports secure and privacy-preserving analysis of personal health data from multiple providers with different governance policies. Our objective is to use this infrastructure to explore the relation between Type 2 Diabetes Mellitus status and healthcare costs. Our approach involves the use of distributed machine learning to analyze vertically partitioned data from the Maastricht Study, a prospective population-based cohort study, and data from the official statistics agency of the Netherlands, Statistics Netherlands (Centraal Bureau voor de Statistiek; CBS). This project seeks an optimal solution accounting for scientific, technical, and ethical/legal challenges. We describe these challenges, our progress towards addressing them in a practical use case, and a simulation experiment. |
