TY - JOUR
T1 - PRELIMINARY DATA ANALYSIS IN HEALTHCARE MULTICENTRIC DATA MINING
T2 - A PRIVACY-PRESERVING DISTRIBUTED APPROACH
AU - Damiani, Andrea
AU - Masciocchi, Carlotta
AU - Boldrini, Luca
AU - Gatta, Roberto
AU - Dinapoli, Nicola
AU - Lenkowicz, Jacopo
AU - Chiloiro, Giuditta
AU - Gambacorta, Maria Antonietta
AU - Tagliaferri, Luca
AU - Autorino, Rosa
AU - Pagliara, Monica Maria
AU - Blasi, Maria Antonietta
AU - van Soest, Johan
AU - Dekker, Andre
AU - Valentini, Vincenzo
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - distributed learning
KW - distributed preliminary analysis
KW - privacy-preserving
KW - healthcare
KW - data mining
KW - DECISION-SUPPORT-SYSTEMS
KW - ONCOLOGY
U2 - 10.20368/1971-8829/1454
DO - 10.20368/1971-8829/1454
M3 - Article
SN - 1826-6223
VL - 14
SP - 71
EP - 81
JO - Journal of E-Learning and Knowledge Society
JF - Journal of E-Learning and Knowledge Society
IS - 1
ER -