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Neural hypernetwork approach for pulmonary embolism diagnosis

Overview of attention for article published in BMC Research Notes, October 2015
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2 tweeters

Citations

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16 Dimensions

Readers on

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55 Mendeley
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Title
Neural hypernetwork approach for pulmonary embolism diagnosis
Published in
BMC Research Notes, October 2015
DOI 10.1186/s13104-015-1554-5
Pubmed ID
Authors

Matteo Rucco, David Sousa-Rodrigues, Emanuela Merelli, Jeffrey H Johnson, Lorenzo Falsetti, Cinzia Nitti, Aldo Salvi

Abstract

Hypernetworks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration of relational structure, logic and analytic dynamics. A pulmonary embolism is a blockage of the main artery of the lung or one of its branches, frequently fatal. Our study uses data on 28 diagnostic features of 1427 people considered to be at risk of pulmonary embolism enrolled in the Department of Internal and Subintensive Medicine of an Italian National Hospital "Ospedali Riuniti di Ancona". Patients arrived in the department after a first screening executed by the emergency room. The resulting neural hypernetwork correctly recognized 94 % of those developing pulmonary embolism. This is better than previous results obtained with other methods (statistical selection of features, partial least squares regression, topological data analysis in a metric space). In this work we successfully derived a new integrative approach for the analysis of partial and incomplete datasets that is based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The novelty of this method is that it does not use clinical parameters extracted by imaging analysis.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Portugal 1 2%
Italy 1 2%
Unknown 52 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 15%
Student > Ph. D. Student 8 15%
Researcher 8 15%
Student > Bachelor 8 15%
Student > Doctoral Student 3 5%
Other 9 16%
Unknown 11 20%
Readers by discipline Count As %
Medicine and Dentistry 22 40%
Computer Science 8 15%
Engineering 3 5%
Mathematics 2 4%
Business, Management and Accounting 1 2%
Other 3 5%
Unknown 16 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 July 2017.
All research outputs
#8,793,038
of 11,424,220 outputs
Outputs from BMC Research Notes
#1,603
of 2,485 outputs
Outputs of similar age
#155,802
of 248,284 outputs
Outputs of similar age from BMC Research Notes
#120
of 216 outputs
Altmetric has tracked 11,424,220 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,485 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 248,284 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 216 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.