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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Data-driven approach for creating synthetic electronic medical records
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, October 2010
|
| DOI | 10.1186/1472-6947-10-59 |
| Pubmed ID | |
| Authors |
Anna L Buczak, Steven Babin, Linda Moniz |
| Abstract |
New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed. |
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 % |
|---|---|---|
| Ireland | 1 | 33% |
| United States | 1 | 33% |
| India | 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 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 4% |
| Netherlands | 1 | <1% |
| Germany | 1 | <1% |
| Malta | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 125 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 29 | 22% |
| Student > Master | 24 | 18% |
| Student > Ph. D. Student | 23 | 17% |
| Other | 7 | 5% |
| Student > Bachelor | 7 | 5% |
| Other | 24 | 18% |
| Unknown | 20 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 42 | 31% |
| Medicine and Dentistry | 36 | 27% |
| Engineering | 6 | 4% |
| Social Sciences | 5 | 4% |
| Nursing and Health Professions | 4 | 3% |
| Other | 20 | 15% |
| Unknown | 21 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 02 July 2018.
All research outputs
#4,865,807
of 23,577,654 outputs
Outputs from BMC Medical Informatics and Decision Making
#446
of 2,025 outputs
Outputs of similar age
#21,015
of 100,569 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#5
of 18 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,025 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 77% 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 100,569 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 79% of its contemporaries.
We're also able to compare this research output to 18 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 72% of its contemporaries.