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ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus

Overview of attention for article published in BMC Bioinformatics, November 2014
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Title
ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus
Published in
BMC Bioinformatics, November 2014
DOI 10.1186/s12859-014-0373-3
Pubmed ID
Authors

Zubair Afzal, Ewoud Pons, Ning Kang, Miriam CJM Sturkenboom, Martijn J Schuemie, Jan A Kors

Abstract

BackgroundIn order to extract meaningful information from electronic medical records, such as signs and symptoms, diagnoses, and treatments, it is important to take into account the contextual properties of the identified information: negation, temporality, and experiencer. Most work on automatic identification of these contextual properties has been done on English clinical text. This study presents ContextD, an adaptation of the English ConText algorithm to the Dutch language, and a Dutch clinical corpus.We created a Dutch clinical corpus containing four types of anonymized clinical documents: entries from general practitioners, specialists¿ letters, radiology reports, and discharge letters. Using a Dutch list of medical terms extracted from the Unified Medical Language System, we identified medical terms in the corpus with exact matching. The identified terms were annotated for negation, temporality, and experiencer properties. To adapt the ConText algorithm, we translated English trigger terms to Dutch and added several general and document specific enhancements, such as negation rules for general practitioners¿ entries and a regular expression based temporality module.ResultsThe ContextD algorithm utilized 41 unique triggers to identify the contextual properties in the clinical corpus. For the negation property, the algorithm obtained an F-score from 87% to 93% for the different document types. For the experiencer property, the F-score was 99% to 100%. For the historical and hypothetical values of the temporality property, F-scores ranged from 26% to 54% and from 13% to 44%, respectively.ConclusionsThe ContextD showed good performance in identifying negation and experiencer property values across all Dutch clinical document types. Accurate identification of the temporality property proved to be difficult and requires further work. The anonymized and annotated Dutch clinical corpus can serve as a useful resource for further algorithm development.

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Mendeley readers

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The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Germany 1 1%
Unknown 69 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 23%
Student > Ph. D. Student 16 23%
Student > Master 11 15%
Student > Bachelor 5 7%
Other 3 4%
Other 6 8%
Unknown 14 20%
Readers by discipline Count As %
Computer Science 26 37%
Medicine and Dentistry 12 17%
Linguistics 3 4%
Engineering 2 3%
Agricultural and Biological Sciences 2 3%
Other 9 13%
Unknown 17 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 03 January 2015.
All research outputs
#14,205,797
of 22,772,779 outputs
Outputs from BMC Bioinformatics
#4,722
of 7,273 outputs
Outputs of similar age
#192,092
of 361,775 outputs
Outputs of similar age from BMC Bioinformatics
#75
of 134 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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