↓ Skip to main content

Multivariate modeling to identify patterns in clinical data: the example of chest pain

Overview of attention for article published in BMC Medical Research Methodology, November 2011
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
40 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Multivariate modeling to identify patterns in clinical data: the example of chest pain
Published in
BMC Medical Research Methodology, November 2011
DOI 10.1186/1471-2288-11-155
Pubmed ID
Authors

Oliver Hirsch, Stefan Bösner, Eyke Hüllermeier, Robin Senge, Krzysztof Dembczynski, Norbert Donner-Banzhoff

Abstract

In chest pain, physicians are confronted with numerous interrelationships between symptoms and with evidence for or against classifying a patient into different diagnostic categories. The aim of our study was to find natural groups of patients on the basis of risk factors, history and clinical examination data which should then be validated with patients' final diagnoses.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
Argentina 1 3%
Unknown 38 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 20%
Student > Master 7 18%
Student > Ph. D. Student 6 15%
Student > Bachelor 5 13%
Student > Postgraduate 3 8%
Other 5 13%
Unknown 6 15%
Readers by discipline Count As %
Medicine and Dentistry 10 25%
Computer Science 5 13%
Business, Management and Accounting 3 8%
Mathematics 3 8%
Agricultural and Biological Sciences 2 5%
Other 10 25%
Unknown 7 18%

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 23 November 2011.
All research outputs
#12,707,565
of 16,003,512 outputs
Outputs from BMC Medical Research Methodology
#1,276
of 1,496 outputs
Outputs of similar age
#161,640
of 216,622 outputs
Outputs of similar age from BMC Medical Research Methodology
#74
of 80 outputs
Altmetric has tracked 16,003,512 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,496 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.7. This one is in the 7th percentile – i.e., 7% 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 216,622 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.