IASiS is an EU-funded project that aims to pave the way for precision medical approaches by using knowledge from patient data. The aim is to combine information from medical records, image databases and genome data to enable personalized diagnosis and treatment approaches in two disease areas - lung cancer and Alzheimer's disease.
Precision medicine promises to change health care for patients. Public health services are evolving from a reactive "one size fits all" system to a system of predictive, preventative and precise care. It is expected that a personalized medicine approach will lead to better health outcomes, improved treatments and reduction in toxicity due to variable or adverse drug reactions. The aim of the IASiS project is to take advantage of the opportunities produced by a wave of data that crosses our path and convert these opportunities into actionable information that corresponds with the right treatment for the right type of patient. A current challenge is to combine large, heterogeneous amounts of data that come from many different sources. This outcome would enable making the best decisions, so that diagnosis and treatment for each individual can be personalized. IASiS is testing this approach in two disease areas – lung cancer and Alzheimer's disease – but with the longer-term ambition of making this approach more widely applicable to other disease areas.
IASiS' ambitious vision will be achieved by pursuing specific goals:
Goal 1: To design a uniform conceptual scheme to present all the different sources of available data.
Goal 2: To create an adaptive system that can incrementally manage data and content.
Goal 3: To provide policy makers with utilizable knowledge about disease diagnosis, disease prognosis and disease treatment.
Goal 4: To promote cooperation between physicians and policy makers.
Goal 5: To identify working strategies that protect privacy and build trust.
The basic approach of IASiS is to develop a system that automatically integrates both unstructured and structured data analysis, image analysis and sequential analysis and also integrates all this knowledge into a big data infrastructure. This system will then create a platform that provides an innovative resource of questions and answers that can be used by clinicians to support more efficient and more individual diagnosis and treatment of patients.
Expected project results and impacts
The results of IASiS will have a significant impact on the EU healthcare system, the ICT industry, individual patients and society as a whole. In this context, IASiS pursues the following aims:
- To make Big Data accessible and manageable by applying principles of sharing and reuse, creating a knowledge network by linking heterogeneous data sources together for public health strategy.
- To develop new data-based analytical techniques and advanced simulation methods to study causal mechanisms and improve predictions of the spatial and temporal evolution of diseases and disorders.
- To develop innovative approaches to improve current methods for risk stratification.
- To transform large amounts of data into actionable information for authorities to be able to plan public health measures and implementing a "health in all policies" concept.
- To place prevention strategies based on evidence, to evaluate efficiency and effectiveness of implemented strategies, to give feedback of results to the method development.
- To analyze the efficiency of care pathway management in both primary (i.e. prevention and early diagnosis) and secondary care.
- To align big data and advanced simulation methods to support policy makers with high leverage for public health officials on a number of epidemiological challenges.
- To develop cross-border network coordination and technology integration which facilitate the interoperability between components of the big data value chain.