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Cancer Long Survivors Artificial Intelligence Follow Up

Project description

Data analysis to improve quality of life for cancer survivors

The number of cancer survivors has increased in recent years due to advances in diagnoses and treatment. Ensuring post-treatment quality of life of survivors remains a challenge. The EU-funded CLARIFY project will identify the risk factors for deterioration in a patient at the end of oncological treatment. Specifically, it will collect data about survivors from breast, lung and lymphoma cancer (the most prevalent types) from hospitals in Spain. Using big data and artificial intelligence techniques, it will integrate all data with relevant publicly available biomedical information, as well as information from wearable devices used after the treatment. The data will be analysed to predict patient-specific risk of developing secondary effects and toxicities from their cancer treatments.


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.

Call for proposal


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Sub call



Net EU contribution
€ 371 250,00
Paseo De la Castellana, 280
28046 MADRID

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Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Public bodies (excluding Research Organisations and Secondary or Higher Education Establishments)
Total cost
€ 1 018 750,00

Participants (12)