Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Reihs, R; Müller, H; Sauer, S; Zatloukal, K.
Automatic classification of histopathological diagnoses for building a large scale tissue catalogue.
Health Technol (Berl). 2017; 7(1):81-88
Doi: 10.1007/s12553-016-0169-8
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- Führende Autor*innen der Med Uni Graz
- Reihs Robert
- Co-Autor*innen der Med Uni Graz
- Müller Heimo
- Sauer Stefan
- Zatloukal Kurt
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- Abstract:
- In this paper an automatic classification system for pathological findings is presented. The starting point in our undertaking was a pathologic tissue collection with about 1.4 million tissue samples described by free text records over 23 years. Exploring knowledge out of this "big data" pool is a challenging task, especially when dealing with unstructured data spanning over many years. The classification is based on an ontology-based term extraction and decision tree build with a manually curated classification system. The information extracting system is based on regular expressions and a text substitution system. We describe the generation of the decision trees by medical experts using a visual editor. Also the evaluation of the classification process with a reference data set is described. We achieved an F-Score of 89,7% for ICD-10 and an F-Score of 94,7% for ICD-O classification. For the information extraction of the tumor staging and receptors we achieved am F-Score ranging from 81,8 to 96,8%.
- Find related publications in this database (Keywords)
- Automatic classification
- Text mining
- Decision Trees
- Biobank