Big Data for Precision Medicine



Prof. Dr. Maria-Esther Vidal


EU (Horizon 2020)


2017 – 2020




IASiS is an EU funded project that aims to combine information from medical records, imaging databases and genomics data to enable more personalised diagnosis and treatment approaches in two disease areas – lung cancer and Alzheimer’s disease.

Precision medicine promises to transform the delivery of healthcare to patients. Healthcare is evolving from a reactive “one-size-fits-all” system towards a system of predictive, preventive, and precision care. A personalised medicine approach is expected to lead to better health outcomes, improved treatments, and reduction in toxicity due to variable or adverse drug responses.

The goal of Project IASIS is to seize the opportunity provided by a wave of data heading our way and turn this into actionable information that would match the right treatment with 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.

IASiS is testing 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.

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.


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