Innovating Works
HORIZON-JU-IHI-2023-05-04
HORIZON-JU-IHI-2023-05-04: Maximising the potential of synthetic data generation in healthcare applications
Expected Impact:To exploit the full potential of digitalisation and data exchange in health care, this topic is expected to contribute to the following expected impacts:
Sólo fondo perdido 0 €
European
This call is closed This line is already closed so you can't apply. It closed last day 16-01-2024.
An upcoming call for this aid is expected, the exact start date of call is not yet clear.
Hace más de 16 mes(es) del cierre y aún no tenemos información sobre los proyectos financiados, no parece que se vaya a publicar esta información.
Presentation: Consortium Consortium: Esta ayuda está diseñada para aplicar a ella en formato consorcio.
Minimum number of participants.
This aid finances Proyectos:

Expected Impact:To exploit the full potential of digitalisation and data exchange in health care, this topic is expected to contribute to the following expected impacts:

wider availability of interoperable, synthetic data generation methodologies and/or datasets facilitating research and development of integrated products and services that will benefit patients;improved insight into real-life behaviour and challenges of patients with complex, chronic diseases and co-morbidities thanks to m-health and e-health technologies;advanced analytics / artificial intelligence tools supporting health research and innovation resulting in: a) better clinical decision support for increased accuracy of diagnosis and efficacy of treatment; b) faster prototyping and shorter times-to-market of personalised health interventions; and c) better evidence of the added value from new digital health and AI tools, including reduced risk of bias due to improved methodologies. Expected Outcome:The proposals should contribute to all of the following expected outcomes:

academic and industrial researchers should have access to relevant, robust, and generalisable synthetic data generatio... see more

Expected Impact:To exploit the full potential of digitalisation and data exchange in health care, this topic is expected to contribute to the following expected impacts:

wider availability of interoperable, synthetic data generation methodologies and/or datasets facilitating research and development of integrated products and services that will benefit patients;improved insight into real-life behaviour and challenges of patients with complex, chronic diseases and co-morbidities thanks to m-health and e-health technologies;advanced analytics / artificial intelligence tools supporting health research and innovation resulting in: a) better clinical decision support for increased accuracy of diagnosis and efficacy of treatment; b) faster prototyping and shorter times-to-market of personalised health interventions; and c) better evidence of the added value from new digital health and AI tools, including reduced risk of bias due to improved methodologies. Expected Outcome:The proposals should contribute to all of the following expected outcomes:

academic and industrial researchers should have access to relevant, robust, and generalisable synthetic data generation methodologies, including open source when relevant, to create and share pools of synthetic patient data in specific use cases;academic and industrial researchers should have access to relevant, high quality synthetic datasets;thanks to better availability of robust synthetic datasets for training data models, healthcare providers and industry should have a wider range of performant AI-based and other data-driven tools to support diagnostics, personalised treatment decision-making and prediction of health outcomes. Scope:Healthcare research using individual patient data is often constrained due to restrictions in data access because of privacy, security, intellectual property (IP) and other concerns. Synthetic health data, i.e., data that is artificially created to mimic individual patient data, can reduce these concerns, leading to more rapid development of reliable data-driven methods including diagnostic, precision medicine, decision support and patient monitoring tools. However, while many synthetic data generation (SDG) methods are currently available, it is not always clear which method is best for which use case, and SDG methods for some types of data are still immature. Furthermore, it is still unclear whether highly detailed synthetic data, which are often needed for research, can be categorised as anonymous.

To address these challenges and maximise the opportunity offered by synthetic data, projects funded under this topic should address the following objectives:

assemble a cross-sectoral public-private consortium including synthetic data experts, public and private data owners, and healthcare solution developers;using high-quality public and private datasets, develop / further develop and validate reliable SDG methods for relevant healthcare use cases. The use cases to be explored must be described and justified in the proposal, complement work that is already ongoing, and should: ensure the broad applicability of the SDG methods developed and include data types that are not currently adequately addressed, such as device data, image data, genomic data etc;include methods to generate: a) fully synthetic datasets that do not contain any real data; b) hybrid datasets composed of a combination of data derived from both real and synthetic data; and c) synthetically-augmented datasets.pay particular attention to bias, both in source data and in the SDG methods. validate the synthetic data generation methods applied in the project using source data. This should include assessing the risk of re-identification;demonstrate the quality and applicability of the synthetic data generated in the project through the development of relevant models;encourage the uptake of the results of the project through a strong communication and outreach plan. Applicants are expected to consider allocating appropriate resources to explore synergies with other relevant initiatives and projects, including the EC proposal for an European Health Data Space (EHDS)1when it becomes operational.

1 https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en

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Temáticas Obligatorias del proyecto: Temática principal: Artificial intelligence Clinical data Health data Imaging image and data processing Machine learning statistical data processing and applications using signal processing (e.g. speech image video)

Consortium characteristics

Scope European : The aid is European, you can apply to this line any company that is part of the European Community.
Tipo y tamaño de organizaciones: The necessary consortium design for the processing of this aid needs:

characteristics of the Proyecto

Requisitos de diseño: Duración:
Requisitos técnicos: Expected Impact:To exploit the full potential of digitalisation and data exchange in health care, this topic is expected to contribute to the following expected impacts: Expected Impact:To exploit the full potential of digitalisation and data exchange in health care, this topic is expected to contribute to the following expected impacts:
Financial Chapters: The chapters of financing expenses for this line are:
Personnel costs.
Expenses related to personnel working directly on the project are based on actual hours spent, based on company costs, and fixed ratios for certain employees, such as the company's owners.
Subcontracting costs.
Payments to external third parties to perform specific tasks that cannot be performed by the project beneficiaries.
Purchase costs.
They include the acquisition of equipment, amortization, material, licenses or other goods and services necessary for the execution of the project
Other cost categories.
Miscellaneous expenses such as financial costs, audit certificates or participation in events not covered by other categories
Indirect costs.
Overhead costs not directly assignable to the project (such as electricity, rent, or office space), calculated as a fixed 25% of eligible direct costs (excluding subcontracting).
Madurez tecnológica: The processing of this aid requires a minimum technological level in the project of TRL 4:. Los componentes que integran determinado proyecto de innovación han sido identificados y se busca establecer si dichos componentes individuales cuentan con las capacidades para actuar de manera integrada, funcionando conjuntamente en un sistema. + info.
TRL esperado:

Characteristics of financing

Intensidad de la ayuda: Sólo fondo perdido + info
Lost Fund:
0% 25% 50% 75% 100%
For the eligible budget, the intensity of the aid in the form of a lost fund may reach as minimum a 100%.
The funding rate for RIA projects is 100 % of the eligible costs for all types of organizations. The funding rate for RIA projects is 100 % of the eligible costs for all types of organizations.
Guarantees:
does not require guarantees
No existen condiciones financieras para el beneficiario.

Additional information about the call

incentive effect: Esta ayuda no tiene efecto incentivador. + info.
Respuesta Organismo: Se calcula que aproximadamente, la respuesta del organismo una vez tramitada la ayuda es de:
Meses de respuesta:
Muy Competitiva:
non -competitive competitive Very competitive
We do not know the total budget of the line pero en los últimos 6 meses la línea ha concecido
total granted en los últimos 6 meses.
minimis: Esta línea de financiación NO considera una “ayuda de minimis”. You can consult the regulations here.

other advantages

SME seal: Tramitar esta ayuda con éxito permite conseguir el sello de calidad de “sello pyme innovadora”. Que permite ciertas ventajas fiscales.
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