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.
X Demographics
Mendeley readers
Attention Score in Context
| 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. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| India | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 125 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 | 122 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 19% |
| Student > Master | 15 | 12% |
| Researcher | 14 | 11% |
| Student > Bachelor | 14 | 11% |
| Student > Doctoral Student | 9 | 7% |
| Other | 23 | 18% |
| Unknown | 26 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 37 | 30% |
| Computer Science | 17 | 14% |
| Engineering | 9 | 7% |
| Business, Management and Accounting | 4 | 3% |
| Nursing and Health Professions | 3 | 2% |
| Other | 23 | 18% |
| Unknown | 32 | 26% |
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
#14,737,988
of 22,685,926 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,225
of 1,979 outputs
Outputs of similar age
#108,736
of 179,003 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#31
of 42 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,979 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 34th percentile – i.e., 34% 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 179,003 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.