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Decision tree-based learning to predict patient controlled analgesia consumption and readjustment

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2012
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  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
80 Mendeley
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Title
Decision tree-based learning to predict patient controlled analgesia consumption and readjustment
Published in
BMC Medical Informatics and Decision Making, November 2012
DOI 10.1186/1472-6947-12-131
Pubmed ID
Authors

Yuh-Jyh Hu, Tien-Hsiung Ku, Rong-Hong Jan, Kuochen Wang, Yu-Chee Tseng, Shu-Fen Yang

Abstract

Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment.

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

Geographical breakdown

Country Count As %
United States 1 1%
Austria 1 1%
Taiwan 1 1%
Unknown 77 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 24%
Researcher 12 15%
Student > Master 11 14%
Student > Bachelor 9 11%
Other 8 10%
Other 15 19%
Unknown 6 8%
Readers by discipline Count As %
Medicine and Dentistry 28 35%
Computer Science 16 20%
Engineering 7 9%
Business, Management and Accounting 4 5%
Decision Sciences 3 4%
Other 12 15%
Unknown 10 13%

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 15 November 2012.
All research outputs
#7,480,379
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#743
of 1,122 outputs
Outputs of similar age
#71,148
of 137,365 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#113
of 154 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.9. 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 137,365 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.