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.
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 language | English |
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Pages (from-to) | S1055-S1055 |
Number of pages | 1 |
Journal | Radiotherapy and Oncology |
Volume | 133 |
Issue number | Supplement 1 |
DOIs | |
Publication status | Published - 20 May 2019 |
Event | 38th Annual Meeting of the European-Society-for-Radiotherapy-and-Oncology (ESTRO) - Barcelona, SPAIN, Milan, Italy Duration: 26 Apr 2019 → 30 Apr 2019 Conference number: 37 |