| Title |
Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools
|
|---|---|
| Published in |
Journal of Medical Systems, October 2015
|
| DOI | 10.1007/s10916-015-0379-z |
| Pubmed ID | |
| Authors |
M. Isabel Ramos, Juan José Cubillas, Francisco R. Feito |
| Abstract |
The continued availability of products at any store is the major issue in order to provide good customer service. If the store is a drugstore this matter reaches a greater importance, as out of stock of a drug when there is high demand causes problems and tensions in the healthcare system. There are numerous studies of the impact this issue has on patients. The lack of any drug in a pharmacy in certain seasons is very common, especially when some external factors proliferate favoring the occurrence of certain diseases. This study focuses on a particular drug consumed in the city of Jaen, southern Andalucia, Spain. Our goal is to determine in advance the Salbutamol demand. Advanced data mining techniques have been used with spatial variables. These last have a key role to generate an effective model. In this research we have used the attributes that are associated with Salbutamol demand and it has been generated a very accurate prediction model of 5.78% of mean absolute error. This is a very encouraging data considering that the consumption of this drug in Jaen varies 500% from one period to another. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 53 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 25% |
| Student > Ph. D. Student | 12 | 23% |
| Student > Bachelor | 8 | 15% |
| Student > Doctoral Student | 2 | 4% |
| Professor | 2 | 4% |
| Other | 7 | 13% |
| Unknown | 9 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 25% |
| Engineering | 9 | 17% |
| Medicine and Dentistry | 6 | 11% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 9% |
| Social Sciences | 3 | 6% |
| Other | 7 | 13% |
| Unknown | 10 | 19% |
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 18 November 2015.
All research outputs
#15,350,522
of 22,833,393 outputs
Outputs from Journal of Medical Systems
#660
of 1,149 outputs
Outputs of similar age
#166,886
of 284,665 outputs
Outputs of similar age from Journal of Medical Systems
#20
of 42 outputs
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So far Altmetric has tracked 1,149 research outputs from this source. They receive a mean Attention Score of 4.5. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.