The goal of Project IASIS is to seize the opportunity provided by the wave of data heading our way and turn this into actionable information that would allow us to match the right treatment to the right type of patient. A current challenge is that there are large, heterogeneous sets of data ranging from different sources, which if combined would enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. 

The approach being adopted in IASIS is to build a system that automatically integrates both unstructured and structured data analysis, image analysis, sequence analysis, and integrating all this knowledge to a big data infrastructure.  This system will then create a platform that will facilitate an innovative question and answer capacity that can be used by clinicians to support more efficient and personalised diagnosis and treatments for patients. 

IASIS will test this approach in two disease areas – lung cancer and Alzheimer’s disease – but with the longer-term ambition that this approach will be more widely applicable to other disease areas.