H2020
Europeo
ExpectedOutcome:This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination 5 “Unlocking the full potential of new tools, technologies and digital solutions for a healthy society”. To that end, proposals under this topic should aim for delivering results that are directed, tailored and contributing to some of the following expected outcomes:
Clinical researchers use effective health data integration solutions for the classification of the clinical phenotypes.Researchers and/or health care professionals use robust and validated data-driven computational tools to successfully stratify patients.Regulatory bodies approve computer-aided patient stratification strategies to enable personalised diagnosis and/or personalised therapy strategies.Health care professionals adopt evidence-based guidelines for stratification-based patient management superior to the standard-of-care.
Scope:In the era of big and complex data, the challenge remains to make sense of the huge amount of health care research data. Computational approaches hold great potential to enable superior patient stratification strategies to the...
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ExpectedOutcome:This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination 5 “Unlocking the full potential of new tools, technologies and digital solutions for a healthy society”. To that end, proposals under this topic should aim for delivering results that are directed, tailored and contributing to some of the following expected outcomes:
Clinical researchers use effective health data integration solutions for the classification of the clinical phenotypes.Researchers and/or health care professionals use robust and validated data-driven computational tools to successfully stratify patients.Regulatory bodies approve computer-aided patient stratification strategies to enable personalised diagnosis and/or personalised therapy strategies.Health care professionals adopt evidence-based guidelines for stratification-based patient management superior to the standard-of-care.
Scope:In the era of big and complex data, the challenge remains to make sense of the huge amount of health care research data. Computational approaches hold great potential to enable superior patient stratification strategies to the established clinical practice, which in turn are a prerequisite for the development of effective personalised medicine approaches.
The proposals may include a broad range of solutions, such as computational disease models, computational systems medicine approaches, machine-learning algorithms, Virtual Physiological Human, digital twin technologies and/or their combinations, as relevant. The topic covers different stages in the continuum of the innovation path (i.e. translational, pre-clinical, clinical research, validation in the clinical and real-world setting, etc.), as relevant to the objectives of the proposals.
The topic will support the development of the computational models driven by the end users' needs.
Proposals should address several of the following areas:
Establish interdisciplinary research by bridging disciplines and technologies (disease biology, clinical research, data science, -omics tools, computational and mathematical modelling of diseases, advanced statistical and/or AI/machine learning methods, Virtual Physiological Human and/or digital twin technologies).Develop new computational models for the integration of complex health data from multiples sources, including structured and unstructured data.Develop and optimise robust, transparent and accurate computational models to guide patient stratification strategies for improving clinical outcomes.Demonstrate, test and clinically validate such models with respect to their utility to realistically stratify patients with the aim of improving the standard-of-care.The development of new patient stratification strategies guided by computational models and the validation of the new concepts of stratification in pre-clinical and/or clinical studies. The proposals should adhere to the FAIR data[1] principles, adopt data quality standards, data integration operating procedures and GDPR-compliant data sharing/access good practices developed by the European research infrastructures, wherever relevant. In addition, proposals are encouraged to adopt good practices of international standards used in the development of computational models, and make available the tools and solutions developed early. Proposals aiming to develop computational models of high technology readiness level are encouraged to deliver a plan for the regulatory acceptability of their technologies. Early interaction with the relevant regulatory bodies is recommended (i.e. the EMA qualification advice for new technologies, etc.) for the proposals contributing to the development of new medicinal products, improvement of the effectiveness of marketed products and the development of medical devices. The proposals aiming to validate their models as high-risk medical devices in the relevant clinical environment are encouraged to deliver a certification implementation plan.
All projects funded under this topic are strongly encouraged to participate in networking and joint activities, as appropriate. These networking and joint activities could, for example, involve the participation in joint workshops, the exchange of knowledge, the development and adoption of best practices, or joint communication activities. This could also involve networking and joint activities with projects funded under other clusters and pillars of Horizon Europe, or other EU programmes, as appropriate. Therefore, proposals are expected to include a budget for the attendance to regular joint meetings and may consider to cover the costs of any other potential joint activities without the prerequisite to detail concrete joint activities at this stage. The details of these joint activities will be defined during the grant agreement preparation phase. In this regard, the Commission may take on the role of facilitator for networking and exchanges, including with relevant stakeholders, if appropriate. In addition, the proposals will be encouraged to exchange with other successful proposals developing AI algorithms and in silico models under other relevant topics.
Cross-cutting Priorities:Artificial IntelligenceDigital AgendaEOSC and FAIR data
[1]FAIR data are data, which meet principles of findability, accessibility, interoperability, and reusability.
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