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

First Therapy Chatbot Trial Shows AI can…

Dartmouth researchers conducted the first clinical trial of a therapy chatbot powered by generative AI and found that the software resulted in significant improvements in participants' symptoms, according to results...

DeepSeek: The "Watson" to Doct…

DeepSeek is an artificial intelligence (AI) platform built on deep learning and natural language processing (NLP) technologies. Its core products include the DeepSeek-R1 and DeepSeek-V3 models. Leveraging an efficient Mixture...

Stepping Hill Hospital Announced as SPAR…

Stepping Hill Hospital, part of Stockport NHS Foundation Trust, has replaced its bedside units with state-of-the art devices running a full range of information, engagement, communications and productivity apps, to...

DMEA 2025: Digital Health Worldwide in B…

8 - 10 April 2025, Berlin, Germany. From the AI Act, to the potential of the European Health Data Space, to the power of patient data in Scandinavia - DMEA 2025...