Preliminary Data Analysis in Healthcare Multicentric Data Mining: a Privacy-preserving Distributed Approach

Andrea Damiani, Carlotta Masciocchi, Luca Boldrini, Roberto Gatta, Nicola Dinapoli, Jacopo Lenkowicz, Giuditta Chiloiro, Maria Antonietta Gambacorta, Luca Tagliaferri, Rosa Autorino, Monica Maria Pagliara, Maria Antonietta Blasi, Johan van Soest, Andre Dekker, Vincenzo Valentini

Abstract


The new era of cognitive health care systems offers a large amount of patient data that can be used to develop prediction models and clinical decision support systems. In this frame, the multi-institutional approach is strongly encouraged in order to reach more numerous samples for data mining and more reliable statistics. For these purposes, shared ontologies need to be developed for data management to ensure database semantic coherence in accordance with the various centers’ ethical and legal policies. Therefore, we propose a privacy-preserving distributed approach as a preliminary data analysis tool to identify possible compliance issues and heterogeneity from the agreed multi-institutional research protocol before training a clinical prediction model. This kind of preliminary analysis appeared fast and reliable and its results corresponded to those obtained using the traditional centralized approach. A real time interactive dashboard has also been presented to show analysis results and make the workflow swifter and easier.

Keywords


Distributed Learning, Privacy preserving, Data anlytics

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DOI: https://doi.org/10.20368/1971-8829/1454



Journal of e-Learning and Knowledge Society | ISSN (online) 1971 - 8829 | ISSN (paper) 1826 - 6223 © 2017 Je-LKS - Italian e-Learning Association (SIe-L).