ALERT

Serious adverse effects resulting from the treatment with thalidomide prompted modern drug legislation more than 40 years ago. Post-marketing spontaneous reporting systems for suspected adverse drug reactions (ADRs) have been a cornerstone to detect safety signals in pharmacovigilance. It has become evident that adverse effects of drugs may be detected too late, when millions of persons have already been exposed.

In this project, an alternative approach for the detection of ADR signals will be developed. Rather than relying on the physician's capability and willingness to recognize and report suspected ADRs, the system will systematically calculate the occurrence of disease (potentially ADRs) during specific drug use based on data available in electronic patient records. In this project, electronic health records (EHRs) of over 30 million patients from several European countries will be available. In an environment where rapid signal detection is feasible, rapid signal assessment is equally important. To rapidly assess signals, a number of resources will be used to substantiate the signals: causal reasoning based on information in the EHRs, semantic mining of the biomedical literature, and computational analysis of biological and chemical information (drugs, targets, anti-targets, SNPs, pathways, etc.).

The overall objective of this project is the design, development and validation of a computerized system that exploits data from electronic healthcare records and biomedical databases for the early detection of adverse drug reactions. The ALERT system will generate signals using data and text mining, epidemiological and other computational techniques, and subsequently substantiate these signals in the light of current knowledge of biological mechanisms and in silico prediction capabilities. The system should be able to detect signals better and faster than spontaneous reporting systems and should allow for identification of subpopulations at higher risk for ADRs.

For further information, please visit:

Project co-ordinator:
ERASMUS UNIVERSITAIR MEDISCH CENTRUM ROTTERDAM

Partners:

  • SOCIETA SERVIZI TELEMATICI SRL
  • UNIVERSIDADE DE AVEIRO
  • THE UNIVERSITY OF NOTTINGHAM
  • PHARMO COOPERATIE UA
  • AARHUS UNIVERSITETSHOSPITAL, AARHUS SYGEHUS
  • UNIVERSIDADE DE SANTIAGO DE COMPOSTELA
  • UNIVERSITAT POMPEU FABRA
  • IRCCS CENTRO NEUROLESI BONINO PULEJO
  • FUNDACIO IMIM
  • LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE
  • ASTRAZENECA AB
  • UNIVERSITE VICTOR SEGALEN BORDEAUX II
  • AGENZIA REGIONALE DI SANITA
  • UNIVERSITA DEGLI STUDI DI MILANO - BICOCCA

Timetable: from 02/2008 – to 07/2011

Total cost: € 5.880.600

EC funding: € 4.500.000

Programme Acronym: FP7-ICT

Subprogramme Area: Advanced ICT for risk assessment and patient safety

Contract type: Collaborative project (generic)

Related news article:

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...