Open Call SC1-DTH-07-2018: Exploiting the Full Potential of in-Silico Medicine Research for Personalised Diagnostics and Therapies in Cloud-Based Environments

European CommissionThe progress in computer modelling and simulation applied in disease management is a European strength and various Decision Support Systems have been developed for different medical disciplines. While the market is developing today, addressing the need of more precise and personalised diagnostics and treatments, the proposed software tools and platforms often need to further conquer visibility and trust from users and investors to get implemented in the routine clinical practice. The access of researchers to high quality big data and in particular to clinical multi-disciplinary data is crucial for validating the use of new tools and platforms in the right practice context.

Through its new initiatives on digital health and care within the Digital Single Market policy, the European Commission aims at leveraging the potential of big data and high performance computing for the emergence of new personalised prevention and treatments for European citizens. The European Cloud Initiative will facilitate the access of researchers to the newest data managing technologies, High Performance Computing facilities to process data and to a European Open Science Cloud list of ICT services while ensuring the appropriate data safety and protection.

Shared infrastructures, data and services in open cloud-based environments will stimulate the virtual complex experimentations in medicine and the link between researchers and healthcare practitioners, for their common benefit.

Scope

Proposals are expected to develop and validate software tools and devices for diagnostic or treatment based on computational modelling and simulation applied in biology and physiology. The solutions should enable decision making in complex situations and contribute to a more precise and personalised management of diseases in order to reduce the burden of non-communicable diseases, such as cancer.

Computer-based decision making can apply to the choice of drugs, devices or other biomedical products, procedures, interventions, in vitro and in vivo diagnostics methods and tools, or combined diagnostics and treatments. In order to ensure access to large multi-disciplinary high quality data sets and diminish the shortage of relevant data, the teams are expected to use shared infrastructures and e-infrastructures, building on existing capacity and expertise and linking where possible with the European initiatives that manage databases relevant for personal health, such as BBMRI, ELIXIR or EATRIS, as well as with Centres of Excellences for computing applications in the area of biomedicine and bio-molecular research as appropriate. They should demonstrate access to the sufficient and relevant clinical data needed for advanced validations. The work should build on - and contribute to reusable data and computer models. Teams are encouraged to use EOSC services as appropriate and possible.

The Commission considers that proposals requesting a contribution from the EU of between EUR 10 and 15 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts. Expected Impact:

The proposal should provide appropriate indicators to measure its progress and specific impact in the following areas:

  • Better translation of big and multi-disciplinary data into predictors for medical outcome and personalised decision making;
  • New digitised trusted diagnostic and treatment tools, and contributing to digitising clinical workflows;
  • Improved disease management, demonstrated in the specific disease context;
  • Links to other European research infrastructure projects and networks operating in related domains;
  • Contribution to the emergence of a European Data Infrastructure for personalised medicine in the context of the DSM, notably by providing reusable data and computer models for personalised prevention and health treatments;
  • Better data quality, interoperability and standards.

Deadline: 24 April 2018 17:00:00

Deadline Model: single-stage

Type of action: RIA Research and Innovation action

For topic conditions, documents and submission service, please visit:
http://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/sc1-dth-07-2018.html

PS: Find your partners or consortia preparing a project proposal
If you are working on Horizon 2020 research project proposals and you would be interested in a SME partner from Germany, please contact us, we are happy to share our experience, expertise and knowledge. If you need help to identify a potential partner with particular competences, facilities or experience, please join and explore our project, (HEALTH IT) SPACE, at www.healthitspace.eu.

Most Popular Now

AI for Real-Rime, Patient-Focused Insigh…

A picture may be worth a thousand words, but still... they both have a lot of work to do to catch up to BiomedGPT. Covered recently in the prestigious journal Nature...

A "Chemical ChatGPT" for New M…

Researchers from the University of Bonn have trained an AI process to predict potential active ingredients with special properties. Therefore, they derived a chemical language model - a kind of...

Siemens Healthineers co-leads EU Project…

Siemens Healthineers is joining forces with more than 20 industry and public partners, including seven leading stroke hospitals, to improve stroke management for patients all over Europe. With a total...

In 10 Seconds, an AI Model Detects Cance…

Researchers have developed an AI powered model that - in 10 seconds - can determine during surgery if any part of a cancerous brain tumor that could be removed remains...

Does AI Improve Doctors' Diagnoses?

With hospitals already deploying artificial intelligence to improve patient care, a new study has found that using Chat GPT Plus does not significantly improve the accuracy of doctors' diagnoses when...

AI Analysis of PET/CT Images can Predict…

Dr. Watanabe and his teams from Niigata University have revealed that PET/CT image analysis using artificial intelligence (AI) can predict the occurrence of interstitial lung disease, known as a serious...

New Medical AI Tool Identifies more Case…

Investigators at Mass General Brigham have developed an AI-based tool to sift through electronic health records to help clinicians identify cases of long COVID, an often mysterious condition that can...

MEDICA and COMPAMED 2024: Shining a Ligh…

11 - 14 November 2024, Düsseldorf, Germany. Christian Grosser, Director Health & Medical Technologies, is looking forward to events getting under way: "From next Monday to Thursday, we will once again...

Jane Stephenson Joins SPARK TSL as Chief…

Jane Stephenson has joined SPARK TSL as chief executive as the company looks to establish the benefits of SPARK Fusion with trusts looking for deployable solutions to improve productivity. Stephenson joins...

NIH-Developed AI Algorithm Successfully …

Researchers from the National Institutes of Health (NIH) have developed an artificial intelligence (AI) algorithm to help speed up the process of matching potential volunteers to relevant clinical research trials...

500 Patient Images per Second Shared thr…

The image exchange portal, widely known in the NHS as the IEP, is now being used to share as many as 500 images each second - including x-rays, CT, MRI...

MEDICA 2024 and COMPAMED 2024: Medical T…

11 - 14 November 2024, Düsseldorf, Germany. "Meet Health. Future. People." is MEDICA's campaign motto for the future in the new trade fair year 2025. The aptness of the motto...