Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools

  • M. Isabel Ramos
  • Juan José Cubillas
  • Francisco R. Feito
Patient Facing Systems
Part of the following topical collections:
  1. Smart Living in Healthcare and Innovations

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.

Keywords

Salbutamol Pharmacy Spatial variables Data mining GIS 

References

  1. 1.
    Bateman, C., Drug stock-outs: Inept supply-chain management and corruption. SAMJ S Afr Med J 103(9):600–602, 2013. doi:10.7196/SAMJ.7332.PubMedCrossRefGoogle Scholar
  2. 2.
    Kweder, S. L., & Dill, S., Drug shortages: the cycle of quantity and quality. Clin. Pharmacol. Ther., 93(3), 245–251, 2013. doi: 10.1038/clpt.2012.235 10.1038/clpt.2012.235#pmc_ext
  3. 3.
    Chin, R. K., Administrative reports for monitoring pharmacy purchasing. Am J Health-Syst Pharm 41(11):2363–2366, 1984.Google Scholar
  4. 4.
    Ibrahim, N., Wong, I. C., Tomlin, S., Sinha, M. D., Rees, L., and Jani, Y., Epidemiology of medication-related problems in children with kidney disease. Pediatr Nephrol 30(4):623–633, 2015. doi:10.1007/s00467-014-2982-5.PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Tayob, S., Challenges in the management of drug supply in public health centres in the Sedibeng District, Gauteng Province (Doctoral dissertation, University of Limpopo (Medunsa Campus)), 2012.Google Scholar
  6. 6.
    Houben, R. M., Van Boeckel, T. P., Mwinuka, V., Mzumara, P., Branson, K., Linard, C., and Crampin, A. C., Monitoring the impact of decentralised chronic care services on patient travel time in rural Africa-methods and results in Northern Malawi. Int J Health Geogr 11(1):49, 2012. doi:10.1186/1476-072X-11-49.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Fox, Erin R., Burgunda V. Sweet, and Valerie Jensen. Drug shortages: a complex health care crisis. Mayo Clinic Proceedings. Vol. 89. No. 3. Elsevier, 2014. doi: 10.1016/j.mayocp.2013.11.014
  8. 8.
    Vademecum. Inc. Available via: http://www.vademecum.es/principios-activos-Salbutamol-r03cc02. Accessed February 2105.
  9. 9.
    Frampton, J. E., QVA149 (indacaterol/glycopyrronium fixed-dose combination): a review of its use in patients with chronic obstructive pulmonary disease. Drugs 74(4):465–488, 2014.PubMedCrossRefGoogle Scholar
  10. 10.
    Pauwel, S., Romain, A., et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO global initiative for chronic obstructive lung disease (GOLD) workshop summary. Am J Respir Crit Care Med 163(5):1256–76, 2001.CrossRefGoogle Scholar
  11. 11.
    Neidell, M. J., Air pollution, health, and socio-economic status: the effect of outdoor air quality on childhood asthma. J Health Econ 23(6):1209–1236, 2004. doi:10.1016/j.jhealeco.2004.05.002.PubMedCrossRefGoogle Scholar
  12. 12.
    McConnell, Rob, et al. Childhood incident asthma and traffic-related air pollution at home and school. Environ. Health Perspect: 1021–1026, 2010. 10.1289/ehp.090123
  13. 13.
    Li, Y., et al., Air quality and outpatient visits for asthma in adults during the 2008 summer Olympic games in Beijing. Sci Total Environ 408(5):1226–1227, 2010.PubMedCrossRefGoogle Scholar
  14. 14.
    O'Connor, G. T., et al., Acute respiratory health effects of air pollution on children with asthma in US inner cities. J. Allergy Clin. Immunol 121(5):1133–1139, 2008.PubMedCrossRefGoogle Scholar
  15. 15.
    Li, S., et al., Ambient temperature and lung function in children with asthma in Australia. Eur Resp J 43(4):1059–1066, 2014. doi:10.1183/09031936.00079313.CrossRefGoogle Scholar
  16. 16.
    Tosca, M. A., et al., Asthma exacerbation in children: relationship among pollens, weather, and air pollution. Allergol Immunopath 42(4):362–368, 2014. doi:10.1016/j.aller.2013.02.006.CrossRefGoogle Scholar
  17. 17.
    Şahin, B., and Tatar, M., Factors affecting use of resources for asthma patients. J Med Syst 30(5):395–403, 2006. doi:10.1007/s10916-006-9024-1.PubMedCrossRefGoogle Scholar
  18. 18.
    Fernandes, R. M., and Hartling, L., Glucocorticoids for acute viral bronchiolitis in infants and young children. JAMA 311(1):87–88, 2014. doi:10.1002/14651858.CD004878.pub3.PubMedCrossRefGoogle Scholar
  19. 19.
    Yilmaz, O., et al., Allergic rhinitis may impact the recovery of pulmonary function tests after moderate/severe asthma exacerbation in children. Allergy 69(5):652–657, 2014. doi:10.1111/all.12391.PubMedCrossRefGoogle Scholar
  20. 20.
    Bellazzi, R., and Zupan, B., Predictive data mining in clinical medicine: issues and guidelines. Int J Med Inform 77(2):81–97, 2008.PubMedCrossRefGoogle Scholar
  21. 21.
    Hoffman, K., Stein, K. V., Maier, M., Rieder, A., and Dorner, T. E., Access points to the different predictors in a country without a gatekeeping system. Results of a cross-sectional study from Austria. Eur. J. Public Health 23(6):933–939, 2013. doi:10.1093/eurpub/ckt008.CrossRefGoogle Scholar
  22. 22.
    Cubillas, J. J., Ramos, M. I., Feito, F. R., and Ureña, T., An improvement in the appointment scheduling in primary health care centers using data mining. J Med Syst 38(8):1–10, 2014. doi:10.1007/s10916-014-0089-y.CrossRefGoogle Scholar
  23. 23.
    REDIAM. Inc. Available via http://www.cma.junta-andalucia.es/medioambiente/site/web/rediam (accessed 17 feb 2015)
  24. 24.
    INE. Inc. Available via http://www.ine.es (accessed 01 feb 2015)
  25. 25.
    MapInfo v.11.0. User Guide MapInfo v.11.0. Pitney Bowes Software Inc., One Global View, Troy, New York 12180–83399.Google Scholar
  26. 26.
    Grünwald P, Advances in Minimum Description Length: Theory and Applications. In: Jae Myung, Mark A. Pitt, Peter D. Grunwald, eds. MIT Press, 2010.Google Scholar
  27. 27.
    Allen, D. M., and Cady, F. B., Analyzing experimental data by regression. CA: Lifetime Learning Publications, Belmont, 1982.Google Scholar
  28. 28.
    Belsley, D. A., Kuh, E., and Welsch, R. E., Regression diagnostics. Wiley, New York, 1980.CrossRefGoogle Scholar
  29. 29.
    Cameron, A. C., and Trivedi, P. K., Regression analysis of count data. Cambridge University Press, Cambridge, 1988.Google Scholar
  30. 30.
    Dobson AJ, An Introduction to Generalized Linear Models. In Chatfield C and Zidek J, eds. Texts in Statistical Science Series. Chapman & Hall/CRC: 90–100, 2000Google Scholar
  31. 31.
    Bolker, B. M., et al., Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24(3):127–135, 2009. doi:10.1016/j.tree.2008.10.008.PubMedCrossRefGoogle Scholar
  32. 32.
    Dibike, Y. B., et al., Model induction with support vector machines: introduction and applications. J. Comput. Civ. Eng 15(3):208–216, 2001. doi:10.1061/(ASCE)0887-3801(2001)15:3(208).CrossRefGoogle Scholar
  33. 33.
    Press WH, Teukolsky SA, Vetterling WT et al., Section Support vector machines. In Press WH, Teukolsky SA, Vetterling WT and Flannery BP, eds. Numerical recipes: The Art of Scientific Computing. New York: Cambridge University: 16.5, 2007.Google Scholar
  34. 34.
    Cristianini N, Shawe-Taylor J, An introduction to support vector machines and other kernel based methods. In Cristianini N and Shawe-Taylor J. Cambridge: Cambridge University Press: 6, 2000Google Scholar
  35. 35.
    Oracle. Inc. Available via http://docs.oracle.com/database/121/DMPRG/toc.htm. (Accessed February 2105)

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • M. Isabel Ramos
    • 1
  • Juan José Cubillas
    • 2
  • Francisco R. Feito
    • 3
  1. 1.Department of Cartography, Geodesy and Photogrammetry EngineeringUniversity of JaenJaenSpain
  2. 2.Department of Computer Science, TIC-144 Andalusian Research Plan (PAI)University of JaenJaenSpain
  3. 3.Department of Computer ScienceUniversity of JaenJaenSpain

Personalised recommendations