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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A practical approach for incorporating dependence among fields in probabilistic record linkage
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, August 2013
|
| DOI | 10.1186/1472-6947-13-97 |
| Pubmed ID | |
| Authors |
Joanne K Daggy, Huiping Xu, Siu L Hui, Roland E Gamache, Shaun J Grannis |
| Abstract |
Methods for linking real-world healthcare data often use a latent class model, where the latent, or unknown, class is the true match status of candidate record-pairs. This commonly used model assumes that agreement patterns among multiple fields within a latent class are independent. When this assumption is violated, various approaches, including the most commonly proposed loglinear models, have been suggested to account for conditional dependence. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 9% |
| Brazil | 1 | 3% |
| Unknown | 30 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 29% |
| Student > Master | 8 | 24% |
| Student > Ph. D. Student | 6 | 18% |
| Other | 4 | 12% |
| Professor > Associate Professor | 2 | 6% |
| Other | 4 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 10 | 29% |
| Mathematics | 5 | 15% |
| Medicine and Dentistry | 5 | 15% |
| Business, Management and Accounting | 3 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 6% |
| Other | 6 | 18% |
| Unknown | 3 | 9% |
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 06 September 2013.
All research outputs
#18,345,822
of 22,719,618 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,565
of 1,982 outputs
Outputs of similar age
#149,052
of 199,368 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#41
of 46 outputs
Altmetric has tracked 22,719,618 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 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% 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 199,368 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 46 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.