CLARIFY

Cancer Long Survivors Artificial Intelligence Follow Up

Facts

Management

Prof. (Univ. Simón Bolívar) Dr. Maria-Esther Vidal

Team

Prof. (Univ. Simón Bolívar) Dr. Maria-Esther Vidal (Scientific Data Management Group), Samaneh Jozashoori (Scientific Data Management Group), and Ahmad Sakor (Scientific Data Management Group)

Funding

EU (Horizon 2020)

Duration

January 2020 - December 2022

Description

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. CLARIFY aims at identifying cancer survivors from three prevalent types of cancer, including breast, lung, and lymphomas. The patient data will be collected from different 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 knowledge graphs. The models 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 integrates and analyses 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.

The TIB team leads the tasks required to support the integration of heterogeneous data sources and semantification of Big data on demand. These tasks include: i) Creation of a federation of knowledge graphs; ii) query processing techniques over knowledge graphs; iii) data integration techniques. As the outcomes of these tasks, knowledge-driven infrastructures for ingesting, curating, and semantically describing Big data will be make available. These infrastructures will allow for the exploration of the meaning of the integrated data, as well as the discovery of relevant patterns between patients. Furthermore, the TIB team participated in dissemination and training to circulate the outcomes of CLARIFY across different scientific communities and stakeholders.

Cooperation

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