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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Automated identification of pneumonia in chest radiograph reports in critically ill patients
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, August 2013
|
| DOI | 10.1186/1472-6947-13-90 |
| Pubmed ID | |
| Authors |
Vincent Liu, Mark P Clark, Mark Mendoza, Ramin Saket, Marla N Gardner, Benjamin J Turk, Gabriel J Escobar |
| Abstract |
Prior studies demonstrate the suitability of natural language processing (NLP) for identifying pneumonia in chest radiograph (CXR) reports, however, few evaluate this approach in intensive care unit (ICU) patients. |
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 % |
|---|---|---|
| India | 1 | 50% |
| United States | 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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 6% |
| United Kingdom | 3 | 4% |
| Spain | 1 | 1% |
| Unknown | 63 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 17% |
| Student > Master | 9 | 13% |
| Student > Postgraduate | 8 | 11% |
| Student > Bachelor | 7 | 10% |
| Student > Ph. D. Student | 7 | 10% |
| Other | 15 | 21% |
| Unknown | 13 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 37% |
| Computer Science | 12 | 17% |
| Nursing and Health Professions | 4 | 6% |
| Psychology | 3 | 4% |
| Engineering | 3 | 4% |
| Other | 7 | 10% |
| Unknown | 16 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 17 June 2017.
All research outputs
#2,926,577
of 22,716,996 outputs
Outputs from BMC Medical Informatics and Decision Making
#238
of 1,982 outputs
Outputs of similar age
#26,253
of 196,013 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#7
of 41 outputs
Altmetric has tracked 22,716,996 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 87% of its peers.
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 196,013 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.