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  • The specific focus of iHELP is on 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 helps to:
  • (i) determine key risks associated with Pancreatic Cancer
  • (ii) develop predictive models for identified risks
  • (iii) develop adaptive models for targeted prevention and intervention measures
  • Based on these developments, the project selects high-risk individuals that are invited to take part in the pilot activities or digital trials.
  • The digital trials are carried out through user-centric mobile and wearable applications that apply proven usability principles to offer more awareness, more engaging experience for health monitoring, risk assessment and personalised decision support. Close collaboration between clinical and AI experts focus on drawing decision support against identified/predicted risks and providing personalised recommendations (e.g. lifestyle changes, behavioural nudges, screening test etc) to the participants in the digital trials.
  • The iHELP (mobile and wearable) technology solutions help in validating iHELP solutions and raising health related awareness at individual level. The (secondary) data gathered through the mobile and wearable applications (concerning life style, behavioural, social interactions and response to targeted prevention and intervention measures) is integrated with primary data in the standardised HHR format – within a big data platform. Frugal AI-based learning techniques are developed to provide near real-time risk assessment based on the integrated and standardised HHR data. iHELP solutions are targeted at multiple stakeholders, including policy-makers that will get decision support on the design of new screening programs and new guidelines for bringing improvements in clinical and lifestyle aspects.


  • Monitoring, Alerting, Feedback and Evaluation Mechanisms
  • Artificial Intelligence
  • Machine learning