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CLARIFY

Financiado

Cerrado

Cancer Long Survivors Artificial Intelligence Follow Up

There were 17 million new cases of cancer diagnosed worldwide in 2018. Survival rates of cancer patients were rather poor until recent decades, when diagnostic techniques have been improved and novel therapeutic options have been... ver más

30/06/2023

SERMAS

5M€

Presupuesto del proyecto: 5M€

Líder del proyecto

SERVICIO MADRILENO DE SALUD No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-06-30

SC1-DTH-01-2019: Big data and Artificial Intelligenc... Specific Challenge:Currently available methods and strategies for diagnosis and treatment of cancer...

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14 Participantes

SERMAS

1.02M€ | Lider

MOCAY SERVICIOS DE HOSTELERI...

296.30K€ | Participante

TIB

457.00K€ | Participante

FUNDACION PARA LA INVESTIGAC...

N.D. | Participante

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Duración del proyecto: 43 meses Fecha Inicio: 2019-11-05
Fecha Fin: 2023-06-30

Líder del proyecto

SERVICIO MADRILENO DE SALUD No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 5M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto There were 17 million new cases of cancer diagnosed worldwide in 2018. Survival rates of cancer patients were rather poor until recent decades, when diagnostic techniques have been improved and novel therapeutic options have been developed. It is estimated that more than 50% of adult patients diagnosed with cancer live at least 5 years in the US and Europe. This situation leads to a new challenge: to increase the cancer patients’ post-treatment quality of life and well-being. This proposal aims at identifying cancer survivors from three prevalent types of cancer, including breast, lung and lymphomas. The patient data will be collected from different Spanish hospitals and the selection will be based on ongoing health and supportive care needs of the particular patient types. We will determine the personalised factors that predict poor health status after specific oncological treatments. For this aim, Big Data and Artificial Intelligence techniques will be used to integrate all available patient´s information with publicly available relevant biomedical databases as well as information from wearable devices used after the treatment. To predict patient-specific risk of developing secondary effects and toxicities of their cancer treatments, we will build novel models based on statistical relational learning and explainable AI techniques on top of the integrated knowledge graphs. The models will utilise background knowledge of the associated cancer biology and thus will help clinicians to make evidence-based post-treatment decisions in a way that is not possible at all with any existing approach. In summary, CLARIFY proposes to integrate and analyse large volumes of heterogenous multivariate data to facilitate early discovery of risk factors that may deteriorate a patient condition after the end of oncological treatment. This will effectively help to stratify cancer survivors by risk in order to personalize their follow-up by better assessment of their needs.

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