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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Data-driven discovery of seasonally linked diseases from an Electronic Health Records system
|
|---|---|
| Published in |
BMC Bioinformatics, May 2014
|
| DOI | 10.1186/1471-2105-15-s6-s3 |
| Pubmed ID | |
| Authors |
Rachel D Melamed, Hossein Khiabanian, Raul Rabadan |
| Abstract |
Patterns of disease incidence can identify new risk factors for the disease or provide insight into the etiology. For example, allergies and infectious diseases have been shown to follow periodic temporal patterns due to seasonal changes in environmental or infectious agents. Previous work searching for seasonal or other temporal patterns in disease diagnosis rates has been limited both in the scope of the diseases examined and in the ability to distinguish unexpected seasonal patterns. Electronic Health Records (EHR) compile extensive longitudinal clinical information, constituting a unique source for discovery of trends in occurrence of disease. However, the data suffer from inherent biases that preclude an identification of temporal trends. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
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 % |
|---|---|---|
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Ireland | 1 | 2% |
| Unknown | 45 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 17% |
| Student > Master | 8 | 17% |
| Student > Ph. D. Student | 7 | 15% |
| Student > Postgraduate | 5 | 10% |
| Student > Bachelor | 4 | 8% |
| Other | 9 | 19% |
| Unknown | 7 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 14 | 29% |
| Computer Science | 9 | 19% |
| Agricultural and Biological Sciences | 6 | 13% |
| Engineering | 5 | 10% |
| Biochemistry, Genetics and Molecular Biology | 3 | 6% |
| Other | 4 | 8% |
| Unknown | 7 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 06 October 2014.
All research outputs
#18,379,655
of 22,765,347 outputs
Outputs from BMC Bioinformatics
#6,307
of 7,273 outputs
Outputs of similar age
#164,015
of 227,078 outputs
Outputs of similar age from BMC Bioinformatics
#113
of 149 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 227,078 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 149 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.