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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Identification of pneumonia and influenza deaths using the death certificate pipeline
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, May 2012
|
| DOI | 10.1186/1472-6947-12-37 |
| Pubmed ID | |
| Authors |
Kailah Davis, Catherine Staes, Jeff Duncan, Sean Igo, Julio C Facelli |
| Abstract |
Death records are a rich source of data, which can be used to assist with public surveillance and/or decision support. However, to use this type of data for such purposes it has to be transformed into a coded format to make it computable. Because the cause of death in the certificates is reported as free text, encoding the data is currently the single largest barrier of using death certificates for surveillance. Therefore, the purpose of this study was to demonstrate the feasibility of using a pipeline, composed of a detection rule and a natural language processor, for the real time encoding of death certificates using the identification of pneumonia and influenza cases as an example and demonstrating that its accuracy is comparable to existing methods. |
X Demographics
The data shown below were collected from the profiles of 3 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 Kingdom | 1 | 33% |
| India | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 48 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 10 | 21% |
| Student > Ph. D. Student | 10 | 21% |
| Researcher | 7 | 15% |
| Student > Doctoral Student | 3 | 6% |
| Student > Bachelor | 3 | 6% |
| Other | 6 | 13% |
| Unknown | 9 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 10 | 21% |
| Social Sciences | 5 | 10% |
| Medicine and Dentistry | 5 | 10% |
| Psychology | 3 | 6% |
| Decision Sciences | 2 | 4% |
| Other | 10 | 21% |
| Unknown | 13 | 27% |
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 27 April 2019.
All research outputs
#13,877,554
of 23,535,927 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,006
of 2,026 outputs
Outputs of similar age
#92,708
of 164,911 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#23
of 40 outputs
Altmetric has tracked 23,535,927 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 2,026 research outputs from this source. They receive a mean Attention Score of 5.0. 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 164,911 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.