Challenges and solutions in the setup of a findings database for a large scale tissue collection R. Reihs * , H. Müller, S. Sauer, K. Zatloukal * Medizin. Universität Graz, Inst. für Pathologie, Austria Objective: Many medical centres have acquired through the last years huge data collections of great relevance for biomedical research. In order to utilize this knowledge it is necessary to analyse this records in a structured way. In our use case the starting point were approximately 1.4 million pathological findings of a non-selected patient group. Method: We developed software tools for the classification of medical records using a phonetic a multi-level spell correction module and an ontology term extraction and decision tree text classification system. With the help of a visual editor a comprehensive decision tree (4174 nodes) was set-up by a team of bioinformaticians and medical experts. Results: With our tool set we achieved in the ICD-10 classification a F-Score of 89,7 % (precession 83,2 % and recall 97,5 %) For the interpretation of the classification results we developed visualization tools and applied the whole software suite at several test cases, e.g. the analysis of a colon data-set with 216.000 findings over 28 years. Conclusion: We developed an automatic classification and visualization system and applied the results in a series of research projects. The software suite is currently used in several research projects and can be easily adjusted to specific phrases used in different institutions and languages.