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The predictability of claim-data-based comorbidity-adjusted models could be improved by using medication data

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2013
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Title
The predictability of claim-data-based comorbidity-adjusted models could be improved by using medication data
Published in
BMC Medical Informatics and Decision Making, November 2013
DOI 10.1186/1472-6947-13-128
Pubmed ID
Authors

Ji Hwan Bang, Soo-Hee Hwang, Eun-Jung Lee, Yoon Kim

Abstract

Recently, claim-data-based comorbidity-adjusted methods such as the Charlson index and the Elixhauser comorbidity measures have been widely used among researchers. At the same time, there have been an increasing number of attempts to improve the predictability of comorbidity-adjusted models. We tried to improve the predictability of models using the Charlson and Elixhauser indices by using medication data; specifically, we used medication data to estimate omitted comorbidities in the claim data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
France 1 3%
Unknown 29 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 16%
Student > Postgraduate 4 13%
Student > Ph. D. Student 4 13%
Student > Master 3 10%
Student > Bachelor 3 10%
Other 5 16%
Unknown 7 23%
Readers by discipline Count As %
Medicine and Dentistry 10 32%
Agricultural and Biological Sciences 2 6%
Neuroscience 2 6%
Psychology 2 6%
Mathematics 1 3%
Other 4 13%
Unknown 10 32%
Attention Score in Context

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 26 November 2013.
All research outputs
#14,639,367
of 22,731,677 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,212
of 1,985 outputs
Outputs of similar age
#180,006
of 302,015 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#32
of 43 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% 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 302,015 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.