Distributed AUC algorithm: a privacy-preserving approach to measure the performance of Cox models

C. Masciocchi, A. Damiani, N. D. Capocchiano, J. Van Soest, J. Lenkowicz, E. Meldolesi, G. Chiloiro, M. A. Gambacorta, A. Dekker, V. Valentini

Research output: Contribution to journalConference Abstract/Poster in journalAcademic

Abstract

Recent years have brought both a notable rise in the
ability to efficiently harvest vast amounts of information,
and a concurrent effort in preserving and actually
enforcing the privacy of patients and their related data,
as evidenced by the European GDPR. In these conditions,
the Distributed Learning Ecosystem has shown great
potential in allowing researchers to pool the huge amounts
of sensitive data need to develop and validate prediction
models in a privacy preserving way and with an eye
towards personalized medicine.
The aim of this abstract is to propose a privacy-preserving
strategy for measuring the performance of Cox
Proportional Hazard (PH) model.
Original languageEnglish
Pages (from-to)S1055-S1055
Number of pages1
JournalRadiotherapy and Oncology
Volume133
Issue numberSupplement 1
DOIs
Publication statusPublished - 20 May 2019
Event38th Annual Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO) - Barcelona, SPAIN, Milan, Italy
Duration: 26 Apr 201930 Apr 2019
Conference number: 37

Cite this

Masciocchi, C., Damiani, A., Capocchiano, N. D., Van Soest, J., Lenkowicz, J., Meldolesi, E., Chiloiro, G., Gambacorta, M. A., Dekker, A., & Valentini, V. (2019). Distributed AUC algorithm: a privacy-preserving approach to measure the performance of Cox models. Radiotherapy and Oncology, 133(Supplement 1), S1055-S1055. https://doi.org/10.1016/S0167-8140(19)32357-6