GAP Project - New Approach to Health Risk Prediction

The GAP Project's goal is to assess the need and the means for a "generic predictive model" for an ICT system capable to produce health alarms at EU level and design a roadmap for the future research on ICT tools for Guard (Detection), Anticipation and Prediction of potential large scale threats. The Project is focused on four types of risks: Biological (communicable diseases and bioterrorism), Natural, Industrial and Nuclear and Terrorist Risks.

The protection of the public's health is one of the most important aims of European countries. In the last decade, many efforts have been made to develop national and international systems for surveillance, anticipation and prediction of threats to public health, utilizing different tools for monitoring, data sharing and modeling. However, the overall technological design of systems that will be able to generate timely alerts and prediction of health threats, and the way to construct them, need still more development.

Investigation of aspects related to ICT research in new risk prediction, assessment and management tools for preparation, surveillance, support and intervention in case of largescale adverse health events was one of the main objectives of the European Commission, through the ICT e-health programme, when launching the GAP (Guard Anticipation & Prediction ) Project.

The GAP Project involves three major steps: Mapping "state of the art" technologies, outlining a generic ICT predictive model, and proposing a roadmap for the research needed to implement it.

The mapping exercise evidenced, among other findings, that there are significant differences between countries for both the processes and the information systems that they use to anticipate health threats; that most of the existing systems focus on surveillance and early detection rather than prediction; and, also, that systems are fragmented and do not function as an integrated structure.

The proposed generic model outlines the characteristics, from the technical and organizational point of view, of a future paneuropean structure, based on interoperability among the actual systems, serving as a tool for management and decision-making at the prediction level.

Currently, the GAP consortium, with the help of key experts, is finalizing the road map that includes the identified key ICT research activities proposed , in order to allow the development of future interoperable prediction systems.

For further information, please visit:
http://www.gaptheproject.eu/

Related article:

  • FP7 Projects: GAP

Most Popular Now

AI Catches One-Third of Interval Breast …

An AI algorithm for breast cancer screening has potential to enhance the performance of digital breast tomosynthesis (DBT), reducing interval cancers by up to one-third, according to a study published...

Great plan: Now We need to Get Real abou…

The government's big plan for the 10 Year Health Plan for the NHS laid out a big role for delivery. However, the Highland Marketing advisory board felt the missing implementation...

Researchers Create 'Virtual Scienti…

There may be a new artificial intelligence-driven tool to turbocharge scientific discovery: virtual labs. Modeled after a well-established Stanford School of Medicine research group, the virtual lab is complete with an...

From WebMD to AI Chatbots: How Innovatio…

A new research article published in the Journal of Participatory Medicine unveils how successive waves of digital technology innovation have empowered patients, fostering a more collaborative and responsive health care...

New AI Tool Accelerates mRNA-Based Treat…

A new artificial intelligence (AI) model can improve the process of drug and vaccine discovery by predicting how efficiently specific mRNA sequences will produce proteins, both generally and in various...

AI also Assesses Dutch Mammograms Better…

AI is detecting tumors more often and earlier in the Dutch breast cancer screening program. Those tumors can then be treated at an earlier stage. This has been demonstrated by...

RSNA AI Challenge Models can Independent…

Algorithms submitted for an AI Challenge hosted by the Radiological Society of North America (RSNA) have shown excellent performance for detecting breast cancers on mammography images, increasing screening sensitivity while...

AI could Help Emergency Rooms Predict Ad…

Artificial intelligence (AI) can help emergency department (ED) teams better anticipate which patients will need hospital admission, hours earlier than is currently possible, according to a multi-hospital study by the...

Head-to-Head Against AI, Pharmacy Studen…

Students pursuing a Doctor of Pharmacy degree routinely take - and pass - rigorous exams to prove competency in several areas. Can ChatGPT accurately answer the same questions? A new...

NHS Active 10 Walking Tracker Users are …

Users of the NHS Active 10 app, designed to encourage people to become more active, immediately increased their amount of brisk and non-brisk walking upon using the app, according to...

New AI Tool Illuminates "Dark Side…

Proteins sustain life as we know it, serving many important structural and functional roles throughout the body. But these large molecules have cast a long shadow over a smaller subclass...

Deep Learning-Based Model Enables Fast a…

Stroke is the second leading cause of death globally. Ischemic stroke, strongly linked to atherosclerotic plaques, requires accurate plaque and vessel wall segmentation and quantification for definitive diagnosis. However, conventional...