| Title |
Automatically Charting Symptoms From Patient-Physician Conversations Using Machine Learning
|
|---|---|
| Published in |
JAMA Internal Medicine, June 2019
|
| DOI | 10.1001/jamainternmed.2018.8558 |
| Pubmed ID | |
| Authors |
Alvin Rajkomar, Anjuli Kannan, Kai Chen, Laura Vardoulakis, Katherine Chou, Claire Cui, Jeffrey Dean |
X Demographics
The data shown below were collected from the profiles of 226 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 States | 89 | 39% |
| United Kingdom | 19 | 8% |
| Spain | 14 | 6% |
| Canada | 5 | 2% |
| France | 4 | 2% |
| Brazil | 3 | 1% |
| Chile | 3 | 1% |
| Switzerland | 3 | 1% |
| Australia | 3 | 1% |
| Other | 23 | 10% |
| Unknown | 60 | 27% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 139 | 62% |
| Scientists | 52 | 23% |
| Practitioners (doctors, other healthcare professionals) | 29 | 13% |
| Science communicators (journalists, bloggers, editors) | 6 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 81 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 15% |
| Student > Master | 10 | 12% |
| Student > Ph. D. Student | 9 | 11% |
| Student > Doctoral Student | 7 | 9% |
| Other | 6 | 7% |
| Other | 17 | 21% |
| Unknown | 20 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 32% |
| Computer Science | 15 | 19% |
| Engineering | 3 | 4% |
| Biochemistry, Genetics and Molecular Biology | 3 | 4% |
| Neuroscience | 2 | 2% |
| Other | 7 | 9% |
| Unknown | 25 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 149. 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 28 January 2020.
All research outputs
#282,864
of 25,832,559 outputs
Outputs from JAMA Internal Medicine
#1,359
of 11,721 outputs
Outputs of similar age
#5,652
of 366,628 outputs
Outputs of similar age from JAMA Internal Medicine
#25
of 114 outputs
Altmetric has tracked 25,832,559 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,721 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 85.2. This one has done well, scoring higher than 88% 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 366,628 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 98% of its contemporaries.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.