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MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank

Overview of attention for article published in Journal of Biomedical Semantics, April 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#21 of 367)
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

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22 X users
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2 Facebook pages
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1 Google+ user

Citations

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62 Dimensions

Readers on

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94 Mendeley
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Title
MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank
Published in
Journal of Biomedical Semantics, April 2017
DOI 10.1186/s13326-017-0123-3
Pubmed ID
Authors

Yuqing Mao, Zhiyong Lu

Abstract

MeSH indexing is the task of assigning relevant MeSH terms based on a manual reading of scholarly publications by human indexers. The task is highly important for improving literature retrieval and many other scientific investigations in biomedical research. Unfortunately, given its manual nature, the process of MeSH indexing is both time-consuming (new articles are not immediately indexed until 2 or 3 months later) and costly (approximately ten dollars per article). In response, automatic indexing by computers has been previously proposed and attempted but remains challenging. In order to advance the state of the art in automatic MeSH indexing, a community-wide shared task called BioASQ was recently organized. We propose MeSH Now, an integrated approach that first uses multiple strategies to generate a combined list of candidate MeSH terms for a target article. Through a novel learning-to-rank framework, MeSH Now then ranks the list of candidate terms based on their relevance to the target article. Finally, MeSH Now selects the highest-ranked MeSH terms via a post-processing module. We assessed MeSH Now on two separate benchmarking datasets using traditional precision, recall and F1-score metrics. In both evaluations, MeSH Now consistently achieved over 0.60 in F-score, ranging from 0.610 to 0.612. Furthermore, additional experiments show that MeSH Now can be optimized by parallel computing in order to process MEDLINE documents on a large scale. We conclude that MeSH Now is a robust approach with state-of-the-art performance for automatic MeSH indexing and that MeSH Now is capable of processing PubMed scale documents within a reasonable time frame. http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/MeSHNow/ .

X Demographics

X Demographics

The data shown below were collected from the profiles of 22 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Germany 1 1%
Unknown 92 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 18%
Student > Master 14 15%
Student > Bachelor 12 13%
Librarian 6 6%
Researcher 6 6%
Other 18 19%
Unknown 21 22%
Readers by discipline Count As %
Computer Science 23 24%
Biochemistry, Genetics and Molecular Biology 8 9%
Medicine and Dentistry 8 9%
Nursing and Health Professions 8 9%
Agricultural and Biological Sciences 5 5%
Other 18 19%
Unknown 24 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 23 November 2017.
All research outputs
#2,234,479
of 25,199,971 outputs
Outputs from Journal of Biomedical Semantics
#21
of 367 outputs
Outputs of similar age
#40,748
of 316,109 outputs
Outputs of similar age from Journal of Biomedical Semantics
#2
of 7 outputs
Altmetric has tracked 25,199,971 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 367 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 94% 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 316,109 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 87% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.