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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A study of diverse clinical decision support rule authoring environments and requirements for integration
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, November 2012
|
| DOI | 10.1186/1472-6947-12-128 |
| Pubmed ID | |
| Authors |
Li Zhou, Neelima Karipineni, Janet Lewis, Saverio M Maviglia, Amanda Fairbanks, Tonya Hongsermeier, Blackford Middleton, Roberto A Rocha |
| Abstract |
Efficient rule authoring tools are critical to allow clinical Knowledge Engineers (KEs), Software Engineers (SEs), and Subject Matter Experts (SMEs) to convert medical knowledge into machine executable clinical decision support rules. The goal of this analysis was to identify the critical success factors and challenges of a fully functioning Rule Authoring Environment (RAE) in order to define requirements for a scalable, comprehensive tool to manage enterprise level rules. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 50% |
| United States | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 5% |
| Norway | 1 | <1% |
| Unknown | 99 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 13% |
| Student > Master | 14 | 13% |
| Student > Ph. D. Student | 13 | 12% |
| Professor > Associate Professor | 8 | 8% |
| Other | 6 | 6% |
| Other | 28 | 27% |
| Unknown | 22 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 30 | 29% |
| Medicine and Dentistry | 24 | 23% |
| Business, Management and Accounting | 7 | 7% |
| Social Sciences | 4 | 4% |
| Engineering | 4 | 4% |
| Other | 11 | 10% |
| Unknown | 25 | 24% |
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 19 January 2014.
All research outputs
#13,371,661
of 22,685,926 outputs
Outputs from BMC Medical Informatics and Decision Making
#979
of 1,979 outputs
Outputs of similar age
#97,553
of 179,649 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#25
of 42 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% 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 47th percentile – i.e., 47% 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 179,649 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% 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 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.