Publication

MedCo(2): Privacy-Preserving Cohort Exploration and Analysis

Jean-Pierre Hubaux, Jean Louis Raisaro, David Jules Froelicher, Mickaël Misbach
2020
Conference Papers

Abstract

Medical studies are usually time consuming, cumbersome and extremely costly to perform, and for exploratory research, their results are also difficult to predict a priori. This is particularly the case for rare diseases, for which finding enough patients is difficult and usually requires an international-scale research. In this case, the process can be even more difficult due to the heterogeneity of data-protection regulations, making the data sharing process particularly hard.

Official source

https://infoscience.epfl.ch/entities/publication/37304734-d92c-458c-9723-7990fd418265

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

MedCo(2): Privacy-Preserving Cohort Exploration and Analysis

Chat with Graph Search

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

Differentially private multi-agent constraint optimization

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles