| Autor: |
Sun, Chang, Ippel, Lianne, van Soest, Johan, Wouters, Birgit, Malic, Alexander, Adekunle, Onaopepo, van den Berg, Bob, Mussmann, Ole, Koster, Annemarie, van der Kallen, Carla, van Oppen, Claudia, Townend, David, Dekker, Andre, Dumontier, Michel |
| Přispěvatelé: |
RS: FSE DACS IDS, Institute of Data Science, RS: FSE Studio Europa Maastricht, RS: FSE BISS, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Radiotherapie, Metamedica, Promovendi PHPC, RS: CAPHRI - R4 - Health Inequities and Societal Participation, Sociale Geneeskunde, Interne Geneeskunde, RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, RS: CARIM - R3 - Vascular biology, Maastricht University Office, RS: FdR Institute IGIR |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Studies in Health Technology and Informatics, 264, 373-377. IOS Press |
| ISSN: |
0926-9630 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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