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A use case study on late stent thrombosis for ontology-based temporal reasoning and analysis

Overview of attention for article published in Journal of Biomedical Semantics, December 2014
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Title
A use case study on late stent thrombosis for ontology-based temporal reasoning and analysis
Published in
Journal of Biomedical Semantics, December 2014
DOI 10.1186/2041-1480-5-49
Pubmed ID
Authors

Kim Clark, Deepak Sharma, Rui Qin, Christopher G Chute, Cui Tao

Abstract

In this paper, we show how we have applied the Clinical Narrative Temporal Relation Ontology (CNTRO) and its associated temporal reasoning system (the CNTRO Timeline Library) to trend temporal information within medical device adverse event report narratives. 238 narratives documenting occurrences of late stent thrombosis adverse events from the Food and Drug Administration's (FDA) Manufacturing and User Facility Device Experience (MAUDE) database were annotated and evaluated using the CNTRO Timeline Library to identify, order, and calculate the duration of temporal events. The CNTRO Timeline Library had a 95% accuracy in correctly ordering events within the 238 narratives. 41 narratives included an event in which the duration was documented, and the CNTRO Timeline Library had an 80% accuracy in correctly determining these durations. 77 narratives included documentation of a duration between events, and the CNTRO Timeline Library had a 76% accuracy in determining these durations. This paper also includes an example of how this temporal output from the CNTRO ontology can be used to verify recommendations for length of drug administration, and proposes that these same tools could be applied to other medical device adverse event narratives in order to identify currently unknown temporal trends.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 19%
Professor 3 14%
Professor > Associate Professor 3 14%
Student > Master 3 14%
Student > Doctoral Student 2 10%
Other 4 19%
Unknown 2 10%
Readers by discipline Count As %
Computer Science 6 29%
Medicine and Dentistry 5 24%
Social Sciences 2 10%
Agricultural and Biological Sciences 1 5%
Nursing and Health Professions 1 5%
Other 1 5%
Unknown 5 24%

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 15 December 2014.
All research outputs
#13,978,796
of 17,520,445 outputs
Outputs from Journal of Biomedical Semantics
#293
of 358 outputs
Outputs of similar age
#216,699
of 313,870 outputs
Outputs of similar age from Journal of Biomedical Semantics
#13
of 13 outputs
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