Document Type : Original Article

Authors

Department of Industrial Management, Science and Research Branch, Tehran, Iran

Abstract

Background: The purpose of this paper is to propose a novel intelligent model for AIDS/HIV
data based on expert system and using it for developing an intelligent medical consulting
system for AIDS/HIV. Materials and Methods: In this descriptive research, 752 frequently asked
questions (FAQs) about AIDS/HIV are gathered from numerous websites about this disease. To
perform the data mining and extracting the intelligent model, the 6 stages of Crisp method has
been completed for FAQs. The 6 stages include: Business understanding, data understanding,
data preparation, modelling, evaluation and deployment. C5.0 Tree classification algorithm is
used for modelling. Also, rational unified process (RUP) is used to develop the web‑based
medical consulting software. Stages of RUP are as follows: Inception, elaboration, construction
and transition. The intelligent developed model has been used in the infrastructure of the
software and based on client’s inquiry and keywords related FAQs are displayed to the client,
according to the rank. FAQs’ ranks are gradually determined considering clients reading it.
Based on displayed FAQs, test and entertainment links are also displayed. Result: The accuracy
of the AIDS/HIV intelligent web‑based medical consulting system is estimated to be 78.76%.
Conclusion: AIDS/HIV medical consulting systems have been developed using intelligent
infrastructure. Being equipped with an intelligent model, providing consulting services on
systematic textual data and providing side services based on client’s activities causes the
implemented system to be unique. The research has been approved by Iranian Ministry of
Health and Medical Education for being practical.

Keywords

1. Shortliffe EH, Perrault LE. Medical informatics: Computer applications
in health care and biomedicine. 2nd ed. New York: Springer; 2000.
2. Edward A, Feigenbaum BG, Buchanan D, Meta D. Roots of
knowledge systems and expert system applications. Artif Intell
1933;59:233‑73.
3. Shorrtliffe EH. Computer‑based medical consultations: MYCIN. New
York: Elsevier; 1976.
4. Aikins JS, Kunz JC, Shortliffe EH, Fallat RJ. PUFF: An expert system
for interpretation of pulmonary function data. Comput Biomed Res
1983;16:199‑208.
5. Hatzilygeroudis I, Vassilakos PJ, Tsakalidis A. XBONE: A hybrid
expert system for supporting diagnosis of bone diseases. Stud
Health Technol Inform 1997;43 Pt A:295‑9.
6. Ghazanfari M, Kazemi Z. Expert systems. Tehran: Elmo Sanat; 2004.
[Persian].
7. François P, Crémilleux B, Robert C, Demongeot J. MENINGE:
A medical consulting system for child’s meningitis. Study on a series
of consecutive cases. Artif Intell Med 1992;4:281‑92.
8. Canfield K. Priming intelligent split menus with text corpora for
computerized patient record data‑entry. Int J Biomed Comput
1995;39:263‑73.
9. Zhou Y, Qin J, Chen H. CMedPort: An integrated approach to
facilitating Chinese medical information seeking. Decis Support
Syst 2006;42:1432‑18.
10. Nematollahi M, Khalesi N, Moghaddasi H, Askarian M. Second
Generation of HIV Surveillance System: A Pattern for Iran. Iran Red
Crescent Med J 2012;14:309‑12.
11. Worobey M, Gemmel M, Teuwen DE, Haselkorn T, Kunstman K,
Bunce M, et al. Direct evidence of extensive diversity of HIV‑1 in
Kinshasa by 1960. Nature 2008;455:661‑4.
12. Fayyad U, Piatetsky‑Shapiro G, Padhraic S. From data mining
to knowledge discovery in databases. American Association for
Artificial Intelligence. 1996;17:37‑18.
13. Horrocks D. CRISP: An introduction. Md Med 2010;11:20‑1.
14. Harper G, Pickett SD. Methods for mining HTS data. Drug Discov
Today 2006;11:694‑9.
15. Hand D, Mannila H, Smith P. Principles of data mining. India: Prentice
Hall; 2005.
16. Fayyad U, Djorgovski S, Weir N. Automating analysis and cataloging
of Sky surveys. In: Fayyad U, Piatetsky-Shapiro G., Smyth P,
Uthurusamy R, editors. Advances in knowledge discovery and
dataminig. Boston: MIT Press; 1996; p. 471-93
17. Kantardzic M. Data mining: Concepts, models and algorithms (Focus
on Machine Learning). New York: IEEE‑Wiley; 2003.
18. Pyle D. Data preparation for data mining. San Francisco, CA: Morgan
Kaufmann; 1999.
19. Emati HR, Barko CD. Key factors for achievinf organizational data
mining success. Ind Manage Data Syst 2003;103:282‑10.
20. Barson A, Smith S, Thearling K. Building data mining applications
for CRM. New York: McGraw Hill; 2000.
21. Han J, Kamber M. Data mining: Concepts and techniques. San
Francisco: Morgan Kaufmann; 2000.
22. Lefebure R, Venture G. Data Mining. Paris: Eyroll; 2000.
23. Larose DT. Discovering Knowledge in Data. New York: John Wiley;
2005.
24. Gómez EJ, Cáceres C, López D, Del Pozo F. A web‑based
self‑monitoring system for people living with HIV/AIDS. Comput
Methods Programs Biomed 2002;69:75‑86.
25. Hull MEC, Taylor PS, Hana JRP, Millar RJ. Software development
process – an assessment. Inf Softw Technol 2002;44:1‑12.