| Title |
Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, September 2020
|
| DOI | 10.1186/s12911-020-01266-z |
| Pubmed ID | |
| Authors |
Wei Tse Li, Jiayan Ma, Neil Shende, Grant Castaneda, Jaideep Chakladar, Joseph C. Tsai, Lauren Apostol, Christine O. Honda, Jingyue Xu, Lindsay M. Wong, Tianyi Zhang, Abby Lee, Aditi Gnanasekar, Thomas K. Honda, Selena Z. Kuo, Michael Andrew Yu, Eric Y. Chang, Mahadevan “ Raj” Rajasekaran, Weg M. Ongkeko |
X Demographics
The data shown below were collected from the profiles of 29 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 | 10% |
| Spain | 3 | 10% |
| South Africa | 1 | 3% |
| United States | 1 | 3% |
| Greece | 1 | 3% |
| China | 1 | 3% |
| Unknown | 19 | 66% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 24 | 83% |
| Scientists | 4 | 14% |
| Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 235 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 235 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 29 | 12% |
| Researcher | 21 | 9% |
| Student > Bachelor | 20 | 9% |
| Other | 18 | 8% |
| Student > Ph. D. Student | 14 | 6% |
| Other | 41 | 17% |
| Unknown | 92 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 39 | 17% |
| Medicine and Dentistry | 31 | 13% |
| Engineering | 13 | 6% |
| Nursing and Health Professions | 11 | 5% |
| Biochemistry, Genetics and Molecular Biology | 6 | 3% |
| Other | 30 | 13% |
| Unknown | 105 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. 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 30 March 2023.
All research outputs
#1,949,984
of 25,593,129 outputs
Outputs from BMC Medical Informatics and Decision Making
#100
of 2,154 outputs
Outputs of similar age
#51,494
of 434,157 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 52 outputs
Altmetric has tracked 25,593,129 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,154 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 95% 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 434,157 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.