Article Sidebar

Submitted
Nov 3, 2018
Published
Jan 31, 2018
Download
PDF
Statistic
Read Counter : 526 Download : 249

Main Article Content

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

Article Details

The author declares that the submitted to Journal of e-Learning and Knowledge Society (Je-LKS) is original and that is has neither been published previously nor is currently being considered for publication elsewhere.
The author agrees that SIe-L (Italian Society of e-Learning) has the right to publish the material sent for inclusion in the journal Je-LKS.
The author agree that articles may be published in digital format (on the Internet or on any digital support and media) and in printed format, including future re-editions, in any language and in any license including proprietary licenses, creative commons license or open access license. SIe-L may also use parts of the work to advertise and promote the publication.
The author declares s/he has all the necessary rights to authorize the editor and SIe-L to publish the work.
The author assures that the publication of the work in no way infringes the rights of third parties, nor violates any penal norms and absolves SIe-L from all damages and costs which may result from publication.

The author declares further s/he has received written permission without limits of time, territory, or language from the rights holders for the free use of all images and parts of works still covered by copyright, without any cost or expenses to SIe-L.

For all the information please check the Ethical Code of Je-LKS, available at http://www.je-lks.org/index.php/ethical-code

How to Cite
Damiani, A., Masciocchi, C., Boldrini, L., Gatta, R., Dinapoli, N., Lenkowicz, J., Chiloiro, G., Gambacorta, M. A., Tagliaferri, L., Autorino, R., Pagliara, M. M., Blasi, M. A., van Soest, J., Dekker, A., & Valentini, V. (2018). Preliminary Data Analysis in Healthcare Multicentric Data Mining: a Privacy-preserving Distributed Approach. Journal of E-Learning and Knowledge Society, 14(1). https://doi.org/10.20368/1971-8829/1454