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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SN algorithm: analysis of temporal clinical data for mining periodic patterns and impending augury
|
|---|---|
| Published in |
Journal of Clinical Bioinformatics, November 2013
|
| DOI | 10.1186/2043-9113-3-24 |
| Pubmed ID | |
| Authors |
Dipankar Sengupta, Pradeep K Naik |
| Abstract |
EHR (Electronic Health Record) system has led to development of specialized form of clinical databases which enable storage of information in temporal prospective. It has been a big challenge for mining this form of clinical data considering varied temporal points. This study proposes a conjoined solution to analyze the clinical parameters akin to a disease. We have used "association rule mining algorithm" to discover association rules among clinical parameters that can be augmented with the disease. Furthermore, we have proposed a new algorithm, SN algorithm, to map clinical parameters along with a disease state at various temporal points. |
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 % |
|---|---|---|
| Nigeria | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| Spain | 1 | 4% |
| United States | 1 | 4% |
| Unknown | 23 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 27% |
| Student > Ph. D. Student | 4 | 15% |
| Student > Master | 3 | 12% |
| Librarian | 2 | 8% |
| Professor > Associate Professor | 2 | 8% |
| Other | 4 | 15% |
| Unknown | 4 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 10 | 38% |
| Medicine and Dentistry | 3 | 12% |
| Engineering | 2 | 8% |
| Business, Management and Accounting | 1 | 4% |
| Agricultural and Biological Sciences | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 9 | 35% |
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 15 December 2014.
All research outputs
#16,047,334
of 25,373,627 outputs
Outputs from Journal of Clinical Bioinformatics
#26
of 61 outputs
Outputs of similar age
#189,392
of 320,129 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#2
of 4 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one has gotten more attention than average, scoring higher than 52% 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 320,129 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.