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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Detecting inpatient falls by using natural language processing of electronic medical records
|
|---|---|
| Published in |
BMC Health Services Research, December 2012
|
| DOI | 10.1186/1472-6963-12-448 |
| Pubmed ID | |
| Authors |
Shin-ichi Toyabe |
| Abstract |
Incident reporting is the most common method for detecting adverse events in a hospital. However, under-reporting or non-reporting and delay in submission of reports are problems that prevent early detection of serious adverse events. The aim of this study was to determine whether it is possible to promptly detect serious injuries after inpatient falls by using a natural language processing method and to determine which data source is the most suitable for this purpose. |
X Demographics
The data shown below were collected from the profiles of 5 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 | 20% |
| Unknown | 4 | 80% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 2% |
| Unknown | 85 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 16 | 18% |
| Researcher | 12 | 14% |
| Student > Ph. D. Student | 11 | 13% |
| Student > Bachelor | 8 | 9% |
| Lecturer | 6 | 7% |
| Other | 20 | 23% |
| Unknown | 14 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 31 | 36% |
| Computer Science | 15 | 17% |
| Nursing and Health Professions | 11 | 13% |
| Social Sciences | 4 | 5% |
| Engineering | 3 | 3% |
| Other | 7 | 8% |
| Unknown | 16 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 07 December 2012.
All research outputs
#7,569,419
of 23,934,148 outputs
Outputs from BMC Health Services Research
#3,722
of 8,014 outputs
Outputs of similar age
#80,324
of 283,398 outputs
Outputs of similar age from BMC Health Services Research
#59
of 125 outputs
Altmetric has tracked 23,934,148 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 8,014 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 52% 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 283,398 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 125 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.