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Autism spectrum disorder detection from semi-structured and unstructured medical data

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, February 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
27 Mendeley
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Title
Autism spectrum disorder detection from semi-structured and unstructured medical data
Published in
EURASIP Journal on Bioinformatics & Systems Biology, February 2017
DOI 10.1186/s13637-017-0057-1
Pubmed ID
Authors

Jianbo Yuan, Chester Holtz, Tristram Smith, Jiebo Luo

Abstract

Autism spectrum disorder (ASD) is a developmental disorder that significantly impairs patients' ability to perform normal social interaction and communication. Moreover, the diagnosis procedure of ASD is highly time-consuming, labor-intensive, and requires extensive expertise. Although there exists no known cure for ASD, there is consensus among clinicians regarding the importance of early intervention for the recovery of ASD patients. Therefore, to benefit autism patients by enhancing their access to treatments such as early intervention, we aim to develop a robust machine learning-based system for autism detection by using Natural Language Processing techniques based on information extracted from medical forms of potential ASD patients. Our detecting framework involves converting semi-structured and unstructured medical forms into digital format, preprocessing, learning document representation, and finally, classification. Testing results are evaluated against the ground truth set by expert clinicians and the proposed system achieve a 83.4% accuracy and 91.1% recall, which is very promising. The proposed ASD detection framework could significantly simplify and shorten the procedure of ASD diagnosis.

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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 30%
Student > Ph. D. Student 6 22%
Student > Bachelor 3 11%
Researcher 2 7%
Librarian 1 4%
Other 4 15%
Unknown 3 11%
Readers by discipline Count As %
Medicine and Dentistry 6 22%
Psychology 4 15%
Biochemistry, Genetics and Molecular Biology 3 11%
Computer Science 3 11%
Nursing and Health Professions 2 7%
Other 7 26%
Unknown 2 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 December 2017.
All research outputs
#6,752,870
of 12,457,990 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#13
of 51 outputs
Outputs of similar age
#140,725
of 335,686 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#3
of 3 outputs
Altmetric has tracked 12,457,990 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 51 research outputs from this source. They receive a mean Attention Score of 1.7. This one has gotten more attention than average, scoring higher than 72% 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 335,686 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 56% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.