iHELP delivered a novel personalised-healthcare framework that fosters 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 (HHRs). The data in the HHRs were 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 user-centric mobile and wearable applications. The standardised integration of data and recalibration of learning models enabled the development of advanced AI techniques that fostered the timely decision support to all stakeholders in the value chain, including: medical experts and also policy makers using relevant interfaces.
The specific focus of iHELP was on the development of solutions targeting to early identification and mitigation of the risks associated with Pancreatic Cancer based on the application of advance AI-based learning and decision support techniques on the historic (primary) data of Cancer patients gathered from established data banks and cohorts. This analysis enabled the (i) specification of key risks associated with Pancreatic Cancer, (ii) development of predictive models for identified risks, and (iii) development of adaptive models for targeted prevention and intervention measures. Based on the identification of key risks and availability of respective models, the project selected high-risk individuals (from hospital records and other sources) that took part in the pilot activities or digital trials. The digital trials carried out through user-centric mobile and wearable applications that applied proven usability principles to offer more engaging experience for health monitoring, risk assessment and personalised decision support. In addition to providing the personalised monitoring, alerting and decision support mechanisms, the iHELP (mobile and wearable) technology solutions facilitated the validation of its solutions, the assessment of their impact and raising health related awareness at individual level. The (secondary) data gathered through the mobile and wearable applications (concerning lifestyle, behavioural, social interactions and response to targeted prevention and intervention measures) was integrated with primary data in the standardised HHR format – within a big data platform. Recalibrated AI-based learning techniques were developed to provide near real-time risk assessment based on the integration and availability of primary and secondary data in the standardised HHR format. The availability of HHRs provided opportunities to validate iHELP outcomes (e.g. improvements in quality of life, reduced risks etc) through advance analytic functions. iHELP solutions also helped in policy making by providing decision support and social analysis on the design of new screening programs and new guidelines for bringing improvements in clinical, lifestyle and behavioural aspects of the fight against Cancer.
