Title |
Characterizing Subgroups of High-Need, High-Cost Patients Based on Their Clinical Conditions: a Machine Learning-Based Analysis of Medicaid Claims Data
|
---|---|
Published in |
Journal of General Internal Medicine, March 2019
|
DOI | 10.1007/s11606-019-04941-8 |
Pubmed ID | |
Authors |
Sudhakar V. Nuti, Patrick Doupe, Blanca Villanueva, Joseph Scarpa, Emilie Bruzelius, Aaron Baum |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 25% |
United States | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 16 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Doctoral Student | 2 | 13% |
Student > Master | 2 | 13% |
Student > Ph. D. Student | 2 | 13% |
Unspecified | 1 | 6% |
Other | 1 | 6% |
Other | 2 | 13% |
Unknown | 6 | 38% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 3 | 19% |
Veterinary Science and Veterinary Medicine | 1 | 6% |
Unspecified | 1 | 6% |
Agricultural and Biological Sciences | 1 | 6% |
Mathematics | 1 | 6% |
Other | 2 | 13% |
Unknown | 7 | 44% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 10 June 2021.
All research outputs
#13,682,250
of 23,911,072 outputs
Outputs from Journal of General Internal Medicine
#5,022
of 7,806 outputs
Outputs of similar age
#184,713
of 383,883 outputs
Outputs of similar age from Journal of General Internal Medicine
#100
of 159 outputs
Altmetric has tracked 23,911,072 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,806 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 383,883 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 50% of its contemporaries.
We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.