This interdisciplinary project proposes the co-creation of three clinical use cases involving seven medical centers located in the EU and beyond, where sensitive patient data is made available and analyzed in a GDPR-compliant mechanism via a Distributed Analytics (DA) paradigm called the Personal Health Train (PHT).
Predictive analytics in healthcare has revolutionized medical decision-making by enabling early disease detection, risk stratification, and personalized treatment plans. However, the implementation of predictive analytics relies on robust data engineering processes to handle the vast amounts of structured and unstructu...
Sophia White· International Journal of Art...· 0 citations
Any need to use a two-stage analysis approach has a negative effect compared to doing a one-stage analysis, and technical and data governance solutions to support one-stage analyses are recommended.
Stella Maris Fabiane, Sharon B. Love, D. Fisher et al.· International Journal of Pop...· 0 citations
The findings demonstrate that ontology-driven approaches significantly improve semantic interoperability by aligning heterogeneous standards, enhance data integration and query efficiency across distributed datasets, and enable scalable real-time analytics for continuous patient monitoring.
Ritesh Chandra, Sonali Agarwal, Navjot Singh et al.· Knowledge and Information Sy...· 0 citations
This scoping review aimed to map federated analytical approaches relevant to the production of NCD indicators within the EHDS and to develop a conceptual framework linking distributed statistical methods, privacy-preserving infrastructures, and policy-oriented surveillance requirements.
Fabrizio Carinci, Stephen Fava, Iztok Štotl et al.· Frontiers in Public Health· 0 citations
This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer to highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics.
Irwan Bastian, Aqilla Rahman Musyaffa, Lukman Nulhakim et al.· IAES International Journal o...· 0 citations
This work expands upon the privacy threat assessment model to quantitatively evaluate the risks of data likability, identifiability, non-repudiation, detectability, unintended disclosure, indulgence, and policy & consent noncompliance, and constructs a framework aimed at mitigating these identified risks.
Jamila Alsayed Kassem, Tim Müller, Christopher A. Esterhuyse et al.· 0 citations
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