| Title |
Applying data mining techniques to improve diagnosis in neonatal jaundice
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, December 2012
|
| DOI | 10.1186/1472-6947-12-143 |
| Pubmed ID | |
| Authors |
Duarte Ferreira, Abílio Oliveira, Alberto Freitas |
| Abstract |
Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 60% |
| Mexico | 1 | 20% |
| India | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Practitioners (doctors, other healthcare professionals) | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Indonesia | 1 | <1% |
| Unknown | 103 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 21 | 20% |
| Researcher | 14 | 13% |
| Student > Ph. D. Student | 12 | 12% |
| Student > Bachelor | 11 | 11% |
| Student > Doctoral Student | 8 | 8% |
| Other | 25 | 24% |
| Unknown | 13 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 25% |
| Computer Science | 22 | 21% |
| Nursing and Health Professions | 8 | 8% |
| Engineering | 6 | 6% |
| Agricultural and Biological Sciences | 4 | 4% |
| Other | 17 | 16% |
| Unknown | 21 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 09 December 2012.
All research outputs
#6,918,838
of 22,689,790 outputs
Outputs from BMC Medical Informatics and Decision Making
#676
of 1,980 outputs
Outputs of similar age
#73,981
of 277,812 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#23
of 45 outputs
Altmetric has tracked 22,689,790 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,980 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 64% 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 277,812 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 72% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.