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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, June 2018
|
| DOI | 10.1186/s12911-018-0620-z |
| Pubmed ID | |
| Authors |
Sara Bersche Golas, Takuma Shibahara, Stephen Agboola, Hiroko Otaki, Jumpei Sato, Tatsuya Nakae, Toru Hisamitsu, Go Kojima, Jennifer Felsted, Sujay Kakarmath, Joseph Kvedar, Kamal Jethwani |
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 % |
|---|---|---|
| Belgium | 2 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 396 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 396 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 59 | 15% |
| Student > Master | 45 | 11% |
| Researcher | 40 | 10% |
| Student > Bachelor | 25 | 6% |
| Student > Doctoral Student | 24 | 6% |
| Other | 67 | 17% |
| Unknown | 136 | 34% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 76 | 19% |
| Computer Science | 61 | 15% |
| Engineering | 24 | 6% |
| Business, Management and Accounting | 16 | 4% |
| Nursing and Health Professions | 15 | 4% |
| Other | 48 | 12% |
| Unknown | 156 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 29. 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 21 June 2023.
All research outputs
#1,371,956
of 26,017,215 outputs
Outputs from BMC Medical Informatics and Decision Making
#56
of 2,164 outputs
Outputs of similar age
#28,626
of 346,119 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 31 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,164 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 97% 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 346,119 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 31 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.