| Title |
Automatic classification of histopathological diagnoses for building a large scale tissue catalogue
|
|---|---|
| Published in |
Health and Technology, December 2016
|
| DOI | 10.1007/s12553-016-0169-8 |
| Pubmed ID | |
| Authors |
Robert Reihs, Heimo Müller, Stefan Sauer, Kurt Zatloukal |
| 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%. |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 15% |
| Researcher | 3 | 15% |
| Student > Master | 3 | 15% |
| Student > Doctoral Student | 2 | 10% |
| Student > Ph. D. Student | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 6 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 25% |
| Medicine and Dentistry | 5 | 25% |
| Nursing and Health Professions | 2 | 10% |
| Agricultural and Biological Sciences | 1 | 5% |
| Decision Sciences | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 5 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 April 2017.
All research outputs
#20,412,387
of 22,962,258 outputs
Outputs from Health and Technology
#213
of 231 outputs
Outputs of similar age
#355,573
of 421,144 outputs
Outputs of similar age from Health and Technology
#10
of 11 outputs
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