An Unsupervised Machine Learning Clustering and Prediction of Differential Clinical Phenotypes of COVID-19 Patients Based on Blood Tests—A Hong Kong Population Study
Pharmacology, Toxicology and Pharmaceutical Science
1
3%
Mathematics
1
3%
Other
4
10%
Unknown
26
67%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 14 March 2022.
All research outputs
#6,597,916
of 23,339,727 outputs
Outputs from Frontiers in Medicine
#1,548
of 5,993 outputs
Outputs of similar age
#136,789
of 442,139 outputs
Outputs of similar age from Frontiers in Medicine
#159
of 657 outputs
Altmetric has tracked 23,339,727 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 5,993 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.5. This one has gotten more attention than average, scoring higher than 73% 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 442,139 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 68% of its contemporaries.
We're also able to compare this research output to 657 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.
