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
IntroductionClinical trials are usually analysed in a single environment allowing for flexible analysis including adjustment or stratification by subgroup: `one-stage' analysis of individual-level data. Health systems datasets, often distributed across geography and providers, can streamline clinical trials. Data are i...
Stella Maris Fabiane, Sharon B. Love, D. Fisher et al.· International Journal of Pop...· 0 citations
Exponential growth in heterogeneous healthcare data arising from electronic health records (EHRs), medical imaging, wearable sensors, and biomedical research has accelerated the adoption of data lakes and centralized architectures capable of handling the volume, variety, and velocity of big data for advanced analytics....
Ritesh Chandra, Sonali Agarwal, Navjot Singh et al.· Knowledge and Information Sy...· 0 citations
Background The growing burden of non-communicable diseases (NCDs) in Europe has intensified the need for timely, comparable, and policy-relevant health indicators derived from increasingly heterogeneous health data ecosystems. The European Health Data Space (EHDS) represents a major policy initiative to facilitate the...
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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