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Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings

Overview of attention for article published in BMC Bioinformatics, August 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
6 tweeters
patent
5 patents
facebook
1 Facebook page

Citations

dimensions_citation
59 Dimensions

Readers on

mendeley
105 Mendeley
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Title
Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings
Published in
BMC Bioinformatics, August 2014
DOI 10.1186/1471-2105-15-266
Pubmed ID
Authors

Anne-Dominique Pham, Aurélie Névéol, Thomas Lavergne, Daisuke Yasunaga, Olivier Clément, Guy Meyer, Rémy Morello, Anita Burgun

Abstract

Natural Language Processing (NLP) has been shown effective to analyze the content of radiology reports and identify diagnosis or patient characteristics. We evaluate the combination of NLP and machine learning to detect thromboembolic disease diagnosis and incidental clinically relevant findings from angiography and venography reports written in French. We model thromboembolic diagnosis and incidental findings as a set of concepts, modalities and relations between concepts that can be used as features by a supervised machine learning algorithm. A corpus of 573 radiology reports was de-identified and manually annotated with the support of NLP tools by a physician for relevant concepts, modalities and relations. A machine learning classifier was trained on the dataset interpreted by a physician for diagnosis of deep-vein thrombosis, pulmonary embolism and clinically relevant incidental findings. Decision models accounted for the imbalanced nature of the data and exploited the structure of the reports.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters 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 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
India 1 <1%
Brazil 1 <1%
Japan 1 <1%
Belgium 1 <1%
Unknown 98 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 21%
Student > Master 19 18%
Student > Ph. D. Student 15 14%
Student > Bachelor 9 9%
Other 6 6%
Other 17 16%
Unknown 17 16%
Readers by discipline Count As %
Medicine and Dentistry 32 30%
Computer Science 23 22%
Engineering 7 7%
Business, Management and Accounting 3 3%
Nursing and Health Professions 2 2%
Other 13 12%
Unknown 25 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 22 March 2022.
All research outputs
#4,232,017
of 20,886,305 outputs
Outputs from BMC Bioinformatics
#1,711
of 6,850 outputs
Outputs of similar age
#42,756
of 208,263 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 18 outputs
Altmetric has tracked 20,886,305 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,850 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 208,263 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.