This will be achieved by:

• Effective automated information extraction from biomedical documents, including improvements using crowd sourcing and active learning, and automated estimation of the level of trust and target user expertise
• Automated analysis and indexing for medical images in 2D (X-Rays), 3D (MRI, CT), and 4D (MRI with a time component)
• Linking information extracted from unstructured or semi-structured biomedical texts and images to structured information in knowledge bases
• Support of cross-language search, including multi-lingual queries, and returning machine-translated pertinent excerpts
• Adaptive user interfaces to assist in formulating queries and display search results via ergonomic and interactive visualizations

Expected impacts

• Medical Impact: Improve the access to medical information for doctors, so that they have more time to talk to and to treat patients, having all the information required for doing so more effectively. Convert the flood of radiological image data into a boon instead of a curse.
• Scientific Impact: Address the lack of publicly available large-scale data sets and realistic task-based scenarios on which to assess new technologies. Make available cutting edge techniques implemented in open source software.
• Industrial Impact: Improve existing open source products’ stability, features and performance, and hence their attractiveness and suitability for wider deployment.
• Public Impact: Members of the public will be using the Health on the Net search engine, improved by the KHRESMOI technology, relatively early in the project.
  

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