title: Automatic classification of histopathological diagnoses for building a large scale tissue catalogue.
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%.
authors.affiliation: Medical University of Graz, A-8010 Graz, Austria.
authors.forename: Robert
authors.identifier: 0000-0001-8439-3689
authors.initials: R
authors.lastname: Reihs
authors.affiliation: Medical University of Graz, A-8010 Graz, Austria.
authors.forename: Heimo
authors.initials: H
authors.lastname: Müller
authors.affiliation: Medical University of Graz, A-8010 Graz, Austria.
authors.forename: Stefan
authors.initials: S
authors.lastname: Sauer
authors.affiliation: Medical University of Graz, A-8010 Graz, Austria.
authors.forename: Kurt
authors.initials: K
authors.lastname: Zatloukal
keywords: Automatic classification; Biobank; Decision Trees; Text mining