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Deep learning architectures for multi-label classification of intelligent health risk prediction

Overview of attention for article published in BMC Bioinformatics, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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8 X users
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1 patent

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114 Dimensions

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Title
Deep learning architectures for multi-label classification of intelligent health risk prediction
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1898-z
Pubmed ID
Authors

Andrew Maxwell, Runzhi Li, Bei Yang, Heng Weng, Aihua Ou, Huixiao Hong, Zhaoxian Zhou, Ping Gong, Chaoyang Zhang

Abstract

Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging problem that warrants further studies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 179 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 179 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 27 15%
Student > Ph. D. Student 26 15%
Researcher 18 10%
Student > Bachelor 15 8%
Student > Doctoral Student 7 4%
Other 22 12%
Unknown 64 36%
Readers by discipline Count As %
Computer Science 50 28%
Engineering 19 11%
Medicine and Dentistry 11 6%
Biochemistry, Genetics and Molecular Biology 6 3%
Agricultural and Biological Sciences 5 3%
Other 18 10%
Unknown 70 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 22 April 2021.
All research outputs
#3,903,655
of 24,453,338 outputs
Outputs from BMC Bioinformatics
#1,316
of 7,535 outputs
Outputs of similar age
#80,245
of 451,183 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 141 outputs
Altmetric has tracked 24,453,338 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,535 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 82% 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 451,183 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 82% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.