Doctors around the world will soon have a powerful new tool at their disposal in the fight against HIV and other infectious diseases: a virtual laboratory that will help them match drugs to patients and make treatments more effective. The ViroLab Virtual Laboratory, the core components of which are scheduled to be available online in 2010, uses the latest advances in machine learning, data mining, grid computing, modelling and simulation to turn the content of millions of scientific journal articles, disparate databases and patients’ own medical histories into knowledge that can effectively be used to treat disease. Developed by a multidisciplinary team of European researchers working in the EU-funded ViroLab project, the virtual laboratory is already being used in seven hospitals to provide personalised treatment to HIV patients and is eliciting widespread interest as a potent decision-support tool for doctors. The ViroLab Virtual Laboratory uses a combination of technologies and methods to help doctors make decisions about the best medication to give each individual patient, accessed through a simple-to-use web interface.
The system continuously crawls grid-connected databases of virological, immunological, clinical, genetic and experimental data, extracts information from scientific journal articles (such as the results of drug resistance experiments) and draws on other sources of information. This data is then processed to give it machine-readable semantic meaning and analysed to produce models of the likely effects of different drugs on a given patient. Each medication is ranked according to its predicted effectiveness in light of the patient’s personal medical history. Crucially, the system incorporates the concept of provenance, ensuring that every step a doctor takes in creating a workflow to find the right drug for a patient and every step the system takes to provide a recommendation is recorded. Because of the distributed nature of the virtual laboratory, cases can be compared to those of other patients living a few streets or thousands of kilometres away. And the system can even generate models simulating the likely spread and progression of different mutations of viruses based not only on medical data but also on sociological information.
More information:
http://cordis.europa.eu/ictresults/index.cfm?section=news&tpl=article&BrowsingType=Features&ID=91302
More information:
http://cordis.europa.eu/ictresults/index.cfm?section=news&tpl=article&BrowsingType=Features&ID=91302