| Title |
Diabetic retinopathy risk prediction for fundus examination using sparse learning: a cross-sectional study
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, September 2013
|
| DOI | 10.1186/1472-6947-13-106 |
| Pubmed ID | |
| Authors |
Ein Oh, Tae Keun Yoo, Eun-Cheol Park |
| Abstract |
Blindness due to diabetic retinopathy (DR) is the major disability in diabetic patients. Although early management has shown to prevent vision loss, diabetic patients have a low rate of routine ophthalmologic examination. Hence, we developed and validated sparse learning models with the aim of identifying the risk of DR in diabetic patients. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Unknown | 108 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 15% |
| Student > Bachelor | 15 | 14% |
| Student > Ph. D. Student | 13 | 12% |
| Student > Master | 9 | 8% |
| Student > Postgraduate | 8 | 7% |
| Other | 17 | 16% |
| Unknown | 31 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 24 | 22% |
| Computer Science | 19 | 17% |
| Engineering | 6 | 6% |
| Agricultural and Biological Sciences | 3 | 3% |
| Biochemistry, Genetics and Molecular Biology | 3 | 3% |
| Other | 17 | 16% |
| Unknown | 37 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 17 September 2013.
All research outputs
#17,696,782
of 22,721,584 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,497
of 1,982 outputs
Outputs of similar age
#140,940
of 197,516 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#33
of 42 outputs
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