| Title |
A method for inferring medical diagnoses from patient similarities
|
|---|---|
| Published in |
BMC Medicine, September 2013
|
| DOI | 10.1186/1741-7015-11-194 |
| Pubmed ID | |
| Authors |
Assaf Gottlieb, Gideon Y Stein, Eytan Ruppin, Russ B Altman, Roded Sharan |
| Abstract |
Clinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools. |
X Demographics
The data shown below were collected from the profiles of 10 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 | 3 | 30% |
| United States | 2 | 20% |
| France | 1 | 10% |
| India | 1 | 10% |
| Unknown | 3 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 60% |
| Scientists | 2 | 20% |
| Practitioners (doctors, other healthcare professionals) | 1 | 10% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| Germany | 1 | <1% |
| Austria | 1 | <1% |
| Hong Kong | 1 | <1% |
| Spain | 1 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 113 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 33 | 27% |
| Researcher | 17 | 14% |
| Student > Master | 15 | 12% |
| Other | 9 | 7% |
| Student > Postgraduate | 5 | 4% |
| Other | 22 | 18% |
| Unknown | 20 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 43 | 36% |
| Medicine and Dentistry | 22 | 18% |
| Engineering | 12 | 10% |
| Agricultural and Biological Sciences | 6 | 5% |
| Social Sciences | 3 | 2% |
| Other | 11 | 9% |
| Unknown | 24 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 11 August 2014.
All research outputs
#1,977,668
of 22,719,618 outputs
Outputs from BMC Medicine
#1,331
of 3,410 outputs
Outputs of similar age
#18,454
of 198,166 outputs
Outputs of similar age from BMC Medicine
#30
of 58 outputs
Altmetric has tracked 22,719,618 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,410 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.5. This one has gotten more attention than average, scoring higher than 60% 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 198,166 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.