ASCAPE: An open AI ecosystem to support the quality of life of cancer patients

The latest cancer statistics indicate a decrease in cancer-related mortality. However, due to the growing and ageing population, the absolute number of people living with cancer is set to keep increasing. This paper presents ASCAPE, an open AI infrastructure that takes advantage of the recent advances in Artificial Intelligence (AI) and Machine Learning (ML) to support cancer patients quality of life (QoL). With ASCAPE health stakeholders (e.g. hospitals) can locally process their private medical data and then share the produced knowledge (ML models) through the open AI infrastructure.

AI Approaches in Processing and Using Data in Personalized Medicine

In modern dynamic constantly developing society, more and more people suffer from chronic and serious diseases and doctors and patients need special and sophisticated medical and health support. Accordingly, prominent health stakeholders have recognized the importance of development of such services to make patients life easier. Such support requires the collection of huge amount of patients complex data like clinical, environmental, nutritional, daily activities, variety of data from smart wearable devices, data from clothing equipped with sensors etc. Holistic patients data must be properly aggregated, processed, analyzed, and presented to the doctors and caregivers to recommend adequate treatment and actions to improve patients health related parameters and general wellbeing. Advanced artificial intelligence techniques offer the opportunity to analyze such big data, consume them, and derive new knowledge to support personalized medical decisions. New approaches like those based on advanced machine learning, federated learning, transfer learning, explainable artificial intelligence open new paths for more quality use of health and medical data in future. In this paper, we will present some crucial aspects and characteristic examples in the area of application of a range of artificial intelligence approaches in personalized medical decisions.