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Health and Technology

, Volume 7, Issue 1, pp 81–88

Systems Medicine

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%.

KeywordsAutomatic classification Text mining Decision Trees Biobank This article is part of the Topical collection on Systems Medicine





Autor: Robert Reihs - Heimo Müller - Stefan Sauer - Kurt Zatloukal

Fuente: https://link.springer.com/







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