| Title |
Machine learning to assist clinical decision-making during the COVID-19 pandemic
|
|---|---|
| Published in |
Bioelectronic Medicine, July 2020
|
| DOI | 10.1186/s42234-020-00050-8 |
| Pubmed ID | |
| Authors |
Shubham Debnath, Douglas P. Barnaby, Kevin Coppa, Alexander Makhnevich, Eun Ji Kim, Saurav Chatterjee, Viktor Tóth, Todd J. Levy, Marc d. Paradis, Stuart L. Cohen, Jamie S. Hirsch, Theodoros P. Zanos |
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 % |
|---|---|---|
| United States | 5 | 83% |
| Unknown | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 17% |
| Practitioners (doctors, other healthcare professionals) | 1 | 17% |
| Scientists | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 171 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 16% |
| Student > Bachelor | 24 | 14% |
| Researcher | 19 | 11% |
| Student > Master | 14 | 8% |
| Student > Doctoral Student | 8 | 5% |
| Other | 30 | 18% |
| Unknown | 48 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 33 | 19% |
| Medicine and Dentistry | 23 | 13% |
| Engineering | 13 | 8% |
| Nursing and Health Professions | 11 | 6% |
| Biochemistry, Genetics and Molecular Biology | 6 | 4% |
| Other | 23 | 13% |
| Unknown | 62 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 141. 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 18 November 2020.
All research outputs
#283,219
of 24,791,202 outputs
Outputs from Bioelectronic Medicine
#5
of 127 outputs
Outputs of similar age
#8,881
of 402,680 outputs
Outputs of similar age from Bioelectronic Medicine
#1
of 5 outputs
Altmetric has tracked 24,791,202 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 127 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.4. This one has done particularly well, scoring higher than 96% 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 402,680 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 97% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them