News

Introducing two new major components of the infrastructure: a brand new user interface (UI) that is aimed at easily running analyses, and an algorithm store, used to find and manage your algorithms.

In the ever-evolving landscape of healthcare analytics, our commitment to innovation has led us to the groundbreaking integration of OMOP and vantage6, read about our efforts here!

We are conducting a survey to understand what people would like to learn during a workshop for vantage6. This survey will take about 3 minutes to complete. Your answers are anonymous and will be used to plan our workshop design and content.

In a previous post, we introduced vantage6 version 4.0. Version 4.0 introduces several new tools for algorithms and is intended to improve the algorithm development experience, specifically for Python.

We are pleased to announce the release of vantage6 version 4.0. We named it after Cotopaxi, one of the highest active volcanoes in the world at 5,897m in the beautiful country of Ecuador.

Data flow in vantage6

Vantage6 is being used a lot in research, in this blogpost we highlight a few of these projects.

We had our first, and successful, community meeting this morning.

We were at the Health-RI symposium 2022!

We would like to invite you to the first vantage6 developer community meeting on the 17th of November from 10-12 on Microsoft Teams.

Release part for vantage6 petronas

The federated learning landscape is evolving incredibly fast. The community is pushing its boundaries everyday, opening the chance for partnerships

We are very happy to communicate that we have officially release vantage6 version 2.0! This time, we named it after a Japanese vantage point: Harukas.

On January 29 2021, Dave Hamersma graduated from Twente University on a thesis where he used vantage6 to learn about quality of breast cancer care.

Vantage6 was used in study on oral cavity cancer in Taiwan and the Netherlands.

We are very happy to share that the Personal Health Train was the winner of the Computable Awards 2020 in the category "Zorgproject" (care project)

In Federated Learning, there are two common ways in which data can be partitioned across parties...

How can we trust a Docker container that has been sent to us from a remote party that performs a computation on our privacy sensitive data?

The most obvious application of VANTAGE6 is to do computations on a partitioned dataset. However there are other applications for VANTAGE6

An update on vantage6's secure multi-party computation functionality