| Title |
Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data
|
|---|---|
| Published in |
Pharmaceutical Medicine, May 2019
|
| DOI | 10.1007/s40290-019-00281-4 |
| Pubmed ID | |
| Authors |
Angelo Del Parigi, Wenbo Tang, Dacheng Liu, Christopher Lee, Richard Pratley |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| New Zealand | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 96 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 14% |
| Researcher | 12 | 13% |
| Student > Bachelor | 9 | 9% |
| Student > Doctoral Student | 8 | 8% |
| Student > Ph. D. Student | 8 | 8% |
| Other | 11 | 11% |
| Unknown | 35 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 28 | 29% |
| Computer Science | 6 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 4% |
| Biochemistry, Genetics and Molecular Biology | 3 | 3% |
| Nursing and Health Professions | 3 | 3% |
| Other | 11 | 11% |
| Unknown | 41 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 06 June 2019.
All research outputs
#14,451,320
of 23,150,406 outputs
Outputs from Pharmaceutical Medicine
#93
of 152 outputs
Outputs of similar age
#192,516
of 350,644 outputs
Outputs of similar age from Pharmaceutical Medicine
#6
of 7 outputs
Altmetric has tracked 23,150,406 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 152 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. 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 350,644 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.