CFP last date
20 January 2025
Reseach Article

Using Associations based Method among Prescribed Medications in Egyptian Public Clinics

by Marwa Zakarya, Yehia Helmy, Sherif Kholeif
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 3
Year of Publication: 2016
Authors: Marwa Zakarya, Yehia Helmy, Sherif Kholeif
10.5120/ijca2016912046

Marwa Zakarya, Yehia Helmy, Sherif Kholeif . Using Associations based Method among Prescribed Medications in Egyptian Public Clinics. International Journal of Computer Applications. 154, 3 ( Nov 2016), 7-9. DOI=10.5120/ijca2016912046

@article{ 10.5120/ijca2016912046,
author = { Marwa Zakarya, Yehia Helmy, Sherif Kholeif },
title = { Using Associations based Method among Prescribed Medications in Egyptian Public Clinics },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 3 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 7-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number3/26469-2016912046/ },
doi = { 10.5120/ijca2016912046 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:12.733267+05:30
%A Marwa Zakarya
%A Yehia Helmy
%A Sherif Kholeif
%T Using Associations based Method among Prescribed Medications in Egyptian Public Clinics
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 3
%P 7-9
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Paper based-prescriptions that are not entered electronically in Egyptian public clinics lead to loss the opportunity of association investigation among prescribed medications, track improper prescriptions and handle patient health record. Developing electronic prescription service can assign a positive impact in such case. In this paper, performing data mining on data collected from paper based- prescriptions ordered by the prescriber and entered electronically as data set to analyze the associations among prescribed medications. Eleven association rules were resulted from the assigned prescribed medication in those prescriptions using the FP-Growth (frequent pattern) algorithm. The accuracy of these rules was reviewed by a clinical pharmacist. Among these association rules, Julmentin 1gm and Allergyl tabs are the most associated medications resulted as first ranked then Pirafene syrup and Julmentin 1gm resulted as second ranked. The results of this study indicate that developing electronic prescription service can lead to significant knowledge in prescribing patterns.

References
  1. MCIT, “ICT for Health.” [Online]. Available: http://www.mcit.gov.eg/Digital_Government/ICT_for_Health.
  2. I. Rank and P. C. Gross, “Egypt,” no. 2005, pp. 0–1, 2010.
  3. Z. H. A. Shehata, N. A. Sabri, and A. A. Elmelegy, “Descriptive analysis of medication errors reported to the Egyptian national online reporting system during six months,” J. Am. Med. Informatics Assoc., vol. 23, no. 2, pp. 366–374, 2016.
  4. O. H. Mohamed Ibrahim, “The Impact of Clinical Pharmacist Interventions on Drug and Antibiotic Prescribing in a Teaching Hospital in Cairo,” Pharmacol. & Pharm., vol. 03, no. 04, pp. 458–461, 2012.
  5. M. Ahmadi, M. Samadbeik, and F. Sadoughi, “Modeling of outpatient prescribing process in Iran: A gateway toward electronic prescribing system,” Iran. J. Pharm. Res., vol. 13, no. 2, pp. 725–738, 2014.
  6. N. A. Sabry and M. M. Abbassi, “Impact of a Clinical Pharmacist in the General Hospital : An Egyptian Trial,” no. June, pp. 577–587, 2014.
  7. H. Khalili, S. Farsaei, H. Rezaee, and S. Dashti-Khavidaki, “Role of clinical pharmacists’ interventions in detection and prevention of medication errors in a medical ward,” Int. J. Clin. Pharm., vol. 33, no. 2, pp. 281–284, 2011.
  8. S. Doddi, A. Marathe, S. S. Ravi, and D. C. Torney, “Discovery of Association rules in Medical Data,” Miim, vol. 26, no. 1, pp. 25–34, 2000.
  9. T. Action and P. Guide, “Focus on …,” vol. 8, no. 3, pp. 199–202, 2008.
  10. H. J. Duggirala, J. M. Tonning, E. Smith, R. A. Bright, J. D. Baker, R. Ball, C. Bell, K. Bouri, S. J. Bright-Ponte, T. Botsis, M. Boyer, K. Burkhart, G. S. Condrey, J. J. Chen, S. Chirtel, R. W. Filice, H. Francis, H. Jiang, J. Levine, D. Martin, T. Oladipo, R. O’Neill, L. A. M. Palmer, A. Paredes, G. Rochester, D. Sholtes, H.-L. Wong, Z. Xu, A. Szarfman, and T. Kass-Hout, “Data Mining at FDA,” no. Cvm, pp. 1–24, 2015.
  11. “drugs data repository.” [Online]. Available: www.drugs .com.
  12. “associations based method.” [Online]. Available: http://docs.rapidminer.com/studio/operators/modeling/associations/create_association_rules.html.
  13. “allergyl indications.” [Online]. Available: http://acaai.org/allergies/allergy-treatment/sublingual-immunotherapy-slit/allergy-tablets-sublingual-immunotherapy.
  14. “pirafene indications.” [Online]. Available: http://memphis.com.eg/en/products/injection/73.html.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Association Rules FP-growth method e–prescription service prescribing errors clinical pharmacist Egypt