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© University of Cologne and University Hospital Cologne Institute for Biomedical Informatics
The NUM-DIZ project of the Network of University Medicine (NUM) builds on the preliminary work of the Medical Informatics Initiative (MII), within which Data Integration Centres (DIZ) have been established at most German academic medical centers with the aim of supporting data provision and cross-site data integration and analysis. [...]
[...] "Personalized Medicine for Oncology" (PM⁴Onco) aims to create a permanent infrastructure for the secure use and exchange of data from clinical and biomedical research. The precise analysis of genetic changes in tumors at the respective stage of the disease is crucial so that molecular tumor boards in oncology centers of excellence can recommend the most appropriate individual therapy.
The NUM Routine Data Platform (NUM-RDP) project aims to provide a generic routine data platform. "Routine data" here means clinical routine documentation data from patient care. In the first funding period, the NUM added the option of centralised, cross-institutional data consolidation, storage and output to the existing structures of the Medical Informatics Initiative (MII) for federated data storage and analysis. [...]
The Open Medical Inference (OMI) platform aims to enable the discovery and use of remote AI services. OMI will develop open medical inference protocols and data formats for the semantically interoperable peer-to-peer exchange of multimodal healthcare data and remote AI inference. To this end, a repository of German-wide services will be established with an initial selection of multimodal AI models in order to enable researchers, through Medical Data Integration Centers of German University Hospitals, to access and run the developed AI models remotely.
There are more than 6,000 diseases, each of which is extremely rare. In total, however, several million people are affected in Germany alone. A disease is considered “rare” if it affects no more than five in 10,000 people. The National Register for Rare Diseases (NARSE) serves to record as many affected people as possible in the long term in order to determine the health effects and care structures of these diseases in Germany. [...]
Development of a nationwide standardised, data protection-compliant infrastructure for the storage and provision of COVID-19 research datasets. Among other things, a comprehensive database, data collection tools, use & access procedures and a trust centre are planned. [...]
