↓ Skip to main content

PubCaseFinder: A Case-Report-Based, Phenotype-Driven Differential-Diagnosis System for Rare Diseases

Overview of attention for article published in American Journal of Human Genetics, September 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
66 tweeters

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
34 Mendeley
Title
PubCaseFinder: A Case-Report-Based, Phenotype-Driven Differential-Diagnosis System for Rare Diseases
Published in
American Journal of Human Genetics, September 2018
DOI 10.1016/j.ajhg.2018.08.003
Pubmed ID
Authors

Toyofumi Fujiwara, Yasunori Yamamoto, Jin-Dong Kim, Orion Buske, Toshihisa Takagi

Abstract

Recently, to speed up the differential-diagnosis process based on symptoms and signs observed from an affected individual in the diagnosis of rare diseases, researchers have developed and implemented phenotype-driven differential-diagnosis systems. The performance of those systems relies on the quantity and quality of underlying databases of disease-phenotype associations (DPAs). Although such databases are often developed by manual curation, they inherently suffer from limited coverage. To address this problem, we propose a text-mining approach to increase the coverage of DPA databases and consequently improve the performance of differential-diagnosis systems. Our analysis showed that a text-mining approach using one million case reports obtained from PubMed could increase the coverage of manually curated DPAs in Orphanet by 125.6%. We also present PubCaseFinder (see Web Resources), a new phenotype-driven differential-diagnosis system in a freely available web application. By utilizing automatically extracted DPAs from case reports in addition to manually curated DPAs, PubCaseFinder improves the performance of automated differential diagnosis. Moreover, PubCaseFinder helps clinicians search for relevant case reports by using phenotype-based comparisons and confirm the results with detailed contextual information.

Twitter Demographics

The data shown below were collected from the profiles of 66 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Researcher 5 15%
Student > Bachelor 5 15%
Student > Master 3 9%
Other 2 6%
Other 5 15%
Unknown 7 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 26%
Medicine and Dentistry 5 15%
Nursing and Health Professions 3 9%
Neuroscience 3 9%
Computer Science 3 9%
Other 5 15%
Unknown 6 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 37. 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 08 September 2018.
All research outputs
#684,880
of 17,659,018 outputs
Outputs from American Journal of Human Genetics
#442
of 5,064 outputs
Outputs of similar age
#18,840
of 283,056 outputs
Outputs of similar age from American Journal of Human Genetics
#15
of 40 outputs
Altmetric has tracked 17,659,018 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,064 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.0. This one has done particularly well, scoring higher than 91% 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 283,056 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 40 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 65% of its contemporaries.