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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, August 2014
|
| DOI | 10.1186/1472-6947-14-65 |
| Pubmed ID | |
| Authors |
Courtney Hebert, Chaitanya Shivade, Randi Foraker, Jared Wasserman, Caryn Roth, Hagop Mekhjian, Stanley Lemeshow, Peter Embi |
| Abstract |
Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission. |
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 % |
|---|---|---|
| India | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 3% |
| Argentina | 2 | 1% |
| United Kingdom | 1 | <1% |
| Spain | 1 | <1% |
| Germany | 1 | <1% |
| Unknown | 137 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 19% |
| Student > Ph. D. Student | 26 | 18% |
| Student > Master | 18 | 12% |
| Student > Bachelor | 12 | 8% |
| Other | 10 | 7% |
| Other | 28 | 19% |
| Unknown | 24 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 43 | 29% |
| Computer Science | 16 | 11% |
| Nursing and Health Professions | 12 | 8% |
| Engineering | 8 | 5% |
| Social Sciences | 6 | 4% |
| Other | 22 | 15% |
| Unknown | 39 | 27% |
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 18 August 2014.
All research outputs
#13,917,593
of 22,759,618 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,065
of 1,985 outputs
Outputs of similar age
#115,319
of 229,899 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#21
of 30 outputs
Altmetric has tracked 22,759,618 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 44th percentile – i.e., 44% 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 229,899 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.