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, preclinical,
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’
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
The proposals should adhere to the FAIR data 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
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.