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TOPIC : Big data and Artificial Intelligence for monitoring health status and quality of life after the cancer treatment

Topic identifier: SC1-DTH-01-2019
Publication date: 27 October 2017

Types of action: RIA Research and Innovation action
DeadlineModel:
Planned opening date:
single-stage
16 October 2018
Deadline: 24 April 2019 17:00:00

Time Zone : (Brussels time)
  Horizon 2020 H2020 website
Pillar: Societal Challenges
Work Programme Year: H2020-2018-2020
Topic Description
Specific Challenge:

Currently available methods and strategies for diagnosis and treatment of cancer help clinicians continuously improve quality of care and prevent cancer deaths in the population. Accurate risk assessment, availability of genetic tests, timely diagnosis and effective treatment has created the impression of cancer being a chronic disease that can be cured. However, often rather aggressive treatment, psychological stress (anxiety and depression) can cause physical and psychological problems that may cause long-term after-cure consequences such as similar or other types of cancer, other types of (chronic) diseases and affect the quality of life of a patient. Therefore, the importance of addressing and, if possible, preventing long-term effects of cancer treatment is growing. In addition to patient-reported outcomes such as functional status, symptoms intensity and frequency, multiple domains of well-being and overall satisfaction with life, the use of big data can bring valuable information for monitoring health status and quality of life after the cancer treatment. Big Data can provide new opportunities to define statistical and clinical significance, but present also challenges as it requires specific analytical approaches.

Scope:

Proposals should focus and deliver on how to better acquire, manage, share, model, process and exploit big data using, if appropriate, high performance computing to effectively monitor health status of individual patients, provide overall actionable insights at the point of care and improve quality of life after the cancer treatment. Relevant solutions include for example systems for determining and monitoring (taking also in account gender differences) the combined effects of cancer treatment, environment, lifestyle and genetics on the quality of life, enabling early identification of effects that can cause development of new medical conditions and/or impair the quality of life. Proposals preferably address relevant health economic issues, use patient reported outcome and experience measures (PROMs and PREMs) and take into account the relevant social aspects of health status and quality of life after cancer treatment. Integrated solutions should include suitable approaches towards security and privacy issues.

Information can be collected from traditional sources of health data (cohorts, comprehensive electronic health records or clinical registries, incl. genetic data, validated biomarkers for remission), from new sources of health data (mobile health apps and wearables) and from sources that are usually created for other purposes such as environmental data.

It is important to assure ethical aspects of data, confidentiality, and anonymity of data transfer and engagement of those who collect / code such data in its analysis and interpretation, in order to avoid misinterpretation and inappropriate conclusions by using proper annotation methodologies of the data. Involvement of those who work within healthcare systems, patients, family and relatives, and the general public is needed.

The Commission considers that proposals requesting a contribution from the EU of between EUR 3 and 5 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts. Participation of SMEs is encouraged.

Expected Impact:

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

  • Mapped comprehensive big data in a reachable and manageable way by applying principles for sharing and reusability, creating a network of knowledge by linking translation tools, heterogeneous data sources and biomedical texts for monitoring health status and quality of life after the cancer treatment;
  • Emerging data driven analytics and advanced simulation methods to study causal mechanisms and improve forecasts of ill-health, identification of disease trajectories and relapse;
  • Better and faster means of high quality response to prevent or timely address development of new medical conditions and/or improve the quality of life;
  • Better knowledge for improved patient counselling as well as to improve follow-up of patients;
  • Novel information on health maintenance, onset and course of medical conditions with a view to optimise prevention and treatment;
  • Evidence base for the development of policy strategies for prevention, early diagnosis, therapies as well as addressing health inequalities, support to patient registries at national level;
  • Improved quality of life after cancer treatment, strengthening personal confidence and enhancing employability;
  • Preventative strategies are established which have a real effect of reducing the occurrence of health disorders and co-morbidities associated with cancer treatment.
Cross-cutting Priorities:

Gender
Open Science
Socio-economic science and humanities

Topic conditions and documents

1. Eligible countries: described in Annex A of the Work Programme.
A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon 2020 projects. See the information in the Online Manual.

In recognition of the opening of the US National Institutes of Health’s programmes to European researchers, any legal entity established in the United States of America is eligible to receive Union funding to support its participation in projects supported under this topic

 

2. Eligibility and admissibility conditions: described in Annex B and Annex C of the Work Programme. 

 

Proposal page limits and layout: please refer to Part B of the proposal template in the submission system below.

 

3. Evaluation:

  • Evaluation criteria, scoring and thresholds are described in Annex H of the Work Programme.  
  • Submission and evaluation processes are described in the Online Manual.

4. Indicative time for evaluation and grant agreements:

Information on the outcome of evaluation (single-stage call): maximum 5 months from the deadline for submission.
Signature of grant agreements: maximum 8 months from the deadline for submission.


 

 

5. Proposal templates, evaluation forms and model grant agreements (MGA):

Research and Innovation Action:

Specific provisions and funding rates
Standard proposal template
Standard evaluation form
General MGA - Multi-Beneficiary
Annotated Grant Agreement

 

6. Additional provisions:

Horizon 2020 budget flexibility
Classified information
Technology readiness levels (TRL) – where a topic description refers to TRL, these definitions apply

Members of consortium are required to conclude a consortium agreement, in principle prior to the signature of the grant agreement.

7. Open access must be granted to all scientific publications resulting from Horizon 2020 actions.

Where relevant, proposals should also provide information on how the participants will manage the research data generated and/or collected during the project, such as details on what types of data the project will generate, whether and how this data will be exploited or made accessible for verification and re-use, and how it will be curated and preserved.

Open access to research data
The Open Research Data Pilot has been extended to cover all Horizon 2020 topics for which the submission is opened on 26 July 2016 or later. Projects funded under this topic will therefore by default provide open access to the research data they generate, except if they decide to opt-out under the conditions described in Annex L of the Work Programme. Projects can opt-out at any stage, that is both before and after the grant signature.

Note that the evaluation phase proposals will not be evaluated more favourably because they plan to open or share their data, and will not be penalised for opting out.

Open research data sharing applies to the data needed to validate the results presented in scientific publications. Additionally, projects can choose to make other data available open access and need to describe their approach in a Data Management Plan.

Projects need to create a Data Management Plan (DMP), except if they opt-out of making their research data open access. A first version of the DMP must be provided as an early deliverable within six months of the project and should be updated during the project as appropriate. The Commission already provides guidance documents, including a template for DMPs. See the Online Manual.

Eligibility of costs: costs related to data management and data sharing are eligible for reimbursement during the project duration.

The legal requirements for projects participating in this pilot are in the article 29.3 of the Model Grant Agreement.

 

8. Additional documents:

1. Introduction WP 2018-20
5. Introduction to Leadership in enabling and industrial technologies (LEITs) WP 2018-20
5i. Information and communication technologies (ICT) WP 2018-20
8. Health, demographic change and well-being WP 2018-20
16. Science with and for society WP 2018-20
18. Dissemination, Exploitation and Evaluation WP 2018-20

General annexes to the Work Programme 2018-2020

Legal basis: Horizon 2020 Regulation of Establishment
Legal basis: Horizon 2020 Rules for Participation
Legal basis: Horizon 2020 Specific Programme

 

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