Overview
The 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, and (iii) develop adaptive models for targeted prevention and intervention measures. Based on the identification of key risks and availability of respective models, the project selects high-risk individuals (from hospital records and other sources) 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 engaging experience for health monitoring, risk assessment and personalised decision support. Close collaboration between clinical and AI experts focus on drawing decision support from the prevention and intervention models against identified/predicted risks and providing personalised recommendations (e.g. lifestyle changes, behavioural nudges, screening test etc) to the participants in the digital trials. In addition to providing the personalised monitoring, alerting and decision support mechanisms, 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 integration and availability of primary and secondary data in the standardised HHR format. The availability of HHRs provide opportunities to validate iHELP outcomes (e.g. improvements in quality of life, reduced risks etc) through advance analytic functions. iHELP solutions also help 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.
Start date: 1/1/2021
Duration: 36 months
Team Contributions
DAC research group is the coordinator of iHELP project. In terms of research, group members focus on data modelling and integrated health records for data capture and ingestion from various heterogeneous data sources, ensuring their quality and interoperability across the full data path. Furthermore, DAC group researches on the decision support suite with visual analytics tools, while also contributing to the assessment of the AI-based solutions and of the sociophysical, human and societal factors.