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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, March 2013
|
| DOI | 10.1186/1472-6947-13-30 |
| Pubmed ID | |
| Authors |
Zubair Afzal, Martijn J Schuemie, Jan C van Blijderveen, Elif F Sen, Miriam CJM Sturkenboom, Jan A Kors |
| Abstract |
Distinguishing cases from non-cases in free-text electronic medical records is an important initial step in observational epidemiological studies, but manual record validation is time-consuming and cumbersome. We compared different approaches to develop an automatic case identification system with high sensitivity to assist manual annotators. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 17% |
| Singapore | 1 | 17% |
| United States | 1 | 17% |
| Bosnia and Herzegovina | 1 | 17% |
| India | 1 | 17% |
| Unknown | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Practitioners (doctors, other healthcare professionals) | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 5% |
| United Kingdom | 2 | 2% |
| Canada | 2 | 2% |
| France | 1 | <1% |
| Unknown | 100 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 22 | 20% |
| Researcher | 19 | 17% |
| Student > Master | 14 | 13% |
| Student > Bachelor | 10 | 9% |
| Student > Doctoral Student | 5 | 5% |
| Other | 15 | 14% |
| Unknown | 25 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 27 | 25% |
| Computer Science | 22 | 20% |
| Agricultural and Biological Sciences | 7 | 6% |
| Mathematics | 4 | 4% |
| Engineering | 4 | 4% |
| Other | 15 | 14% |
| Unknown | 31 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 03 March 2014.
All research outputs
#5,857,100
of 22,699,621 outputs
Outputs from BMC Medical Informatics and Decision Making
#518
of 1,980 outputs
Outputs of similar age
#48,252
of 193,968 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#15
of 42 outputs
Altmetric has tracked 22,699,621 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,980 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 73% 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 193,968 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 74% of its contemporaries.
We're also able to compare this research output to 42 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 64% of its contemporaries.