| Title |
COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm
|
|---|---|
| Published in |
Frontiers in Public Health, July 2020
|
| DOI | 10.3389/fpubh.2020.00357 |
| Pubmed ID | |
| Authors |
Celestine Iwendi, Ali Kashif Bashir, Atharva Peshkar, R. Sujatha, Jyotir Moy Chatterjee, Swetha Pasupuleti, Rishita Mishra, Sofia Pillai, Ohyun Jo |
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 % |
|---|---|---|
| India | 2 | 33% |
| Switzerland | 1 | 17% |
| United Kingdom | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Practitioners (doctors, other healthcare professionals) | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 486 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 486 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 52 | 11% |
| Student > Ph. D. Student | 43 | 9% |
| Student > Bachelor | 40 | 8% |
| Researcher | 36 | 7% |
| Other | 19 | 4% |
| Other | 71 | 15% |
| Unknown | 225 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 93 | 19% |
| Engineering | 40 | 8% |
| Medicine and Dentistry | 22 | 5% |
| Biochemistry, Genetics and Molecular Biology | 15 | 3% |
| Mathematics | 9 | 2% |
| Other | 64 | 13% |
| Unknown | 243 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 16 October 2020.
All research outputs
#6,832,978
of 24,163,421 outputs
Outputs from Frontiers in Public Health
#2,476
of 12,135 outputs
Outputs of similar age
#142,158
of 401,380 outputs
Outputs of similar age from Frontiers in Public Health
#75
of 219 outputs
Altmetric has tracked 24,163,421 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 12,135 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has done well, scoring higher than 79% 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 401,380 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 219 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.