ALERTProject reference: 215847
Funded under: FP7-ICT
Early detection of adverse drug events by integrative mining of clinical records and biomedical knowledge
Total cost:EUR 5 880 600
EU contribution:EUR 4 500 000
Subprogramme:ICT-2007.5.2 - Advanced ICT for risk assessment and patient safety
Funding scheme:CP - Collaborative project (generic)
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.
LONDON, United Kingdom
SANTIAGO DE COMPOSTELA, Spain
BORDEAUX CEDEX, France