Descripción del proyecto
Análisis de datos para mejorar la calidad de vida de los supervivientes de cáncer
El número de supervivientes de cáncer ha aumentado en los últimos años gracias a los avances en su diagnóstico y tratamiento, pero garantizar la calidad de vida después del tratamiento sigue siendo un desafío. El proyecto financiado con fondos europeos CLARIFY identificará los factores de riesgo de deterioro en cada paciente al final del tratamiento oncológico. Concretamente, recopilará datos sobre los supervivientes de cáncer de mama, pulmón y linfoma (los tipos con mayor prevalencia) de hospitales españoles. Empleando técnicas de datos masivos e inteligencia artificial, integrará todos los datos con información biomédica pertinente a disposición del público, además de información de dispositivos vestibles utilizados después del tratamiento. Los datos se analizarán para predecir el riesgo de determinados pacientes de desarrollar efectos secundarios y toxicidades derivados de los tratamientos oncológicos recibidos.
Objetivo
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.
Ámbito científico
- natural sciencescomputer and information sciencesartificial intelligence
- natural sciencescomputer and information sciencesinternetinternet of things
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesknowledge engineering
- medical and health sciencesclinical medicineoncology
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-SC1-DTH-2019
Régimen de financiación
RIA - Research and Innovation actionCoordinador
28046 MADRID
España