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Artificial intelligence on the identification of risk groups for osteoporosis, a general review

Overview of attention for article published in BioMedical Engineering OnLine, January 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)

Mentioned by

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3 tweeters
googleplus
1 Google+ user

Citations

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39 Dimensions

Readers on

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160 Mendeley
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Title
Artificial intelligence on the identification of risk groups for osteoporosis, a general review
Published in
BioMedical Engineering OnLine, January 2018
DOI 10.1186/s12938-018-0436-1
Pubmed ID
Authors

Agnaldo S. Cruz, Hertz C. Lins, Ricardo V. A. Medeiros, José M. F. Filho, Sandro G. da Silva

Abstract

The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms "Neural Network", "Osteoporosis Machine Learning" and "Osteoporosis Neural Network". Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000-2017 were selected; however, articles prior to this period with great relevance were included in this study. Based on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented. It is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 160 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 160 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 14%
Student > Ph. D. Student 22 14%
Student > Master 22 14%
Student > Bachelor 17 11%
Student > Doctoral Student 9 6%
Other 26 16%
Unknown 41 26%
Readers by discipline Count As %
Medicine and Dentistry 32 20%
Engineering 24 15%
Nursing and Health Professions 11 7%
Computer Science 11 7%
Biochemistry, Genetics and Molecular Biology 4 3%
Other 23 14%
Unknown 55 34%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 12 February 2018.
All research outputs
#6,626,131
of 20,402,729 outputs
Outputs from BioMedical Engineering OnLine
#181
of 771 outputs
Outputs of similar age
#142,597
of 394,559 outputs
Outputs of similar age from BioMedical Engineering OnLine
#1
of 1 outputs
Altmetric has tracked 20,402,729 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 771 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 64% 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 394,559 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 57% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them