Survival analysis is a branch of statistics for analyzing the expected duration of the time until the event of interest happens. It is not only applicable to biomedical problems but it can be widely used in almost every domain since there is a relevant data structure available. Recent studies have shown that it is a powerful approach for risk stratification. Since it is a well established statistical technique, there have been several studies that combine survival analysis with machine learning algorithms in order to obtain better performances.

In general iHELP: delivers a novel personalised-healthcare framework that enables the collection, integration and management of health-related data from various sources (medical records, lifestyle, behaviours, social media interactions) in a standardised structure called Holistic Health Records. The data in the HHRs is analysed using advance AI techniques to draw adaptive learning models that are used to provide decision support in the form of early risk predictions as well as personalised prevention & intervention measures (alerts, behavioural nudges, consultations medications, therapies, screening etc) that are delivered through usercentric mobile and wearable applications.

PROJECT OVERVIEW: INFINITECH will provide: Novel BigData/IoT technologies for seamless management and querying of all types of data interoperable data analytics, blockchain-based data sharing, real-time analytics, as well as libraries of advanced AI algorithms. Regulatory tools incorporating various data governance capabilities and facilitating compliance to regulations (e.g., PSD2, 4AMLD, MIFiD II). Nine novel and configurable testbeds & sandboxes, each one offering Open APIs and other resources for validating autonomous and personalized solutions, including a unique collection of data assets for finance/insurance.