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Machine Learning For Health - ML4Health

Reference number
Coordinator Karolinska Institutet - Department of Molecular Medicine and Surgery
Funding from Vinnova SEK 199 109
Project duration April 2020 - June 2020
Status Completed

Purpose and goal

The purpose was to plan and compile a project application, ML4HEALTH (Machine Learning for Health), with the aim of submitting the application as a response to the H2020 call "AI for Genomics and Personalized Medicine DT-TDS-04-2020". The MLT4HEALTH project is about developing machine learning methods that enables sharing and analysis of precision medicine data derived from omics- and/or healthcare data that are located in different data sources on an international level. The goal was achieved by the submission of the application on June 18, 2020.

Expected results and effects

The funding of the application preparation project enabled several persons from the research group to participate and contribute to the preparation of the ML4HEALTH-applications. The submitted ML4HEALTH-application consists of 182 pages with an overall budget of 8.4 million Euros. If the ML4HEALTH-ansökan is granted, it is expected to provide an opportunity to pursue the proposed project for a period of four years. For Karolinska Institutet, one person would then be able to participate full time during the full project period.

Planned approach and implementation

The application was prepared through dedicated work in different Work Packages (WPs). The coordination between these groups, and the compilation of the whole application was coordinated by an application coordinator. This application encompasses six WPs. We contributed with the application coordinator, a WP leader and further participants. Thus, we had a relative high participation in the application process. The preparation work was focused, with high participation rate and commitment, and kept a pace high enough to keep the timelines and be submitted on time.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 18 September 2020

Reference number 2020-01808

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