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Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing

Overview of attention for article published in Journal of Digital Imaging, January 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
49 Mendeley
Title
Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing
Published in
Journal of Digital Imaging, January 2017
DOI 10.1007/s10278-016-9931-8
Pubmed ID
Authors

Saeed Hassanpour, Graham Bay, Curtis P. Langlotz

Abstract

We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination of machine learning and rule-based approaches for this purpose. Our method is unique in capturing different features and levels of abstractions at surface, entity, and discourse levels in text analysis. This combination has enabled us to recognize the underlying semantics of radiology report narratives for this task. We evaluated our method on radiology reports from four major healthcare organizations. Our evaluation showed the efficacy of our method in highlighting important changes (accuracy 99.2%, precision 96.3%, recall 93.5%, and F1 score 94.7%) and identifying significant observations (accuracy 75.8%, precision 75.2%, recall 75.7%, and F1 score 75.3%) to characterize radiology reports. This method can help clinicians quickly understand the key observations in radiology reports and facilitate clinical decision support, review prioritization, and disease surveillance.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 48 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 18%
Researcher 9 18%
Student > Master 7 14%
Student > Doctoral Student 6 12%
Student > Bachelor 5 10%
Other 13 27%
Readers by discipline Count As %
Medicine and Dentistry 18 37%
Unspecified 10 20%
Computer Science 10 20%
Agricultural and Biological Sciences 3 6%
Nursing and Health Professions 2 4%
Other 6 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 02 November 2017.
All research outputs
#3,300,261
of 12,088,915 outputs
Outputs from Journal of Digital Imaging
#136
of 560 outputs
Outputs of similar age
#90,098
of 260,383 outputs
Outputs of similar age from Journal of Digital Imaging
#4
of 16 outputs
Altmetric has tracked 12,088,915 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 560 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 75% 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 260,383 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.