Journal of Medical Systems

, Volume 36, Issue 4, pp 2071–2083 | Cite as

Application of Intelligent Systems in Asthma Disease: Designing a Fuzzy Rule-Based System for Evaluating Level of Asthma Exacerbation

  • Maryam Zolnoori
  • Mohammad Hossein Fazel Zarandi
  • Mostafa Moin
ORIGINAL PAPER

Abstract

This paper discusses the capacities of artificial intelligence in the process of asthma diagnosing and asthma treatment. Developed intelligent systems for asthma disease have been classified in five categories including diagnosing, evaluating, management, communicative facilities, and prediction. Considering inputs, results, and methodologies of the systems show that by focusing on meticulous analysis of quality of life as an input variable and developing patient-based systems, under-diagnosing and asthma morbidity and mortality would decrease significantly. Regard to the importance of accurate evaluation in accurate prescription and expeditious treatment, the methodology of developing a fuzzy expert system for evaluating level of asthma exacerbation is presented in this paper too. The performance of this system has been tested in Asthma, Allergy, and Immunology Center of Iran using 25 asthmatic patients. Comparison between system’s results and physicians’ evaluations using Kappa coefficient (K) reinforces the value of K = 1. In addition this system assigns a degree in gradation (0–10) to every patient representing the slight differences between patients assigned to a specific category.

Keywords

Intelligent systems Fuzzy Asthma Asthma exacerbation 

References

  1. 1.
    Stefanelli, M., The socio-organizational age of artificial intelligence in medicine. Artif. Intell. Med. 23(1):25–47, 2001.CrossRefGoogle Scholar
  2. 2.
    Hoong, N. K., Medical information science—framework and potential. international seminar and exhibition computerization for development- the research challenge. Universiti Pertanian Malaysia, Kuala Lumpur, pp. 191–198, 1988.Google Scholar
  3. 3.
    Patel, V. L., Shortliffe, E. H., Stefanelli, M., Szolovits, P., Berthold, M., Bellazzi, R., and Abu-Hanna, A., The coming of age of artificial intelligence in medicine. Artif. Intell. Med. 46(1):5–17, 2009.CrossRefGoogle Scholar
  4. 4.
    Bellazzi, R., and Abu-Hanna, A., artificial intelligence in medicine AIME’07. Artif. Intell. Med. 46:1–3, 2009.CrossRefGoogle Scholar
  5. 5.
    Lai, C., Beasley, R., Crane, J., Foliaki, S., Shah, J., and Weiland, S., Global variation in the prevalence and severity of asthma symptoms: Phase Three of the International Study of Asthma and Allergic in Chilhood (ISAAC). Thorax 64:476–483, 2009.CrossRefGoogle Scholar
  6. 6.
    Guidelines for the Diagnosis and Management of Difficult-to-Control Asthma, Assembly on asthma of the spanish society of pulmonology and thoracic surgery. Arch Bronconeumol 41(9):513–523, 2005.Google Scholar
  7. 7.
    Chakraborty, C., Mitra, T., Mukherjee, A., and Ray, A. K., CAIDSA: computer-aided intelligent diagnosing system for bronchial asthma. Expert system applications 36:4958–4966, 2009.CrossRefGoogle Scholar
  8. 8.
    Yan, D. C., Ou, L. S., Tsai, T. L., Wu, W. F., and Huang, J. L., Prevalence and severity of symptoms of asthma, rhinitis, and eczema in 13- to 14-year-old children in Taipei. Taiwan. Ann Allergy Asthma Immunol 95(6):579–585, 2005.CrossRefGoogle Scholar
  9. 9.
    Masjedi, M. R., Fadaizadeh, L., Najafizadeh, K., and Dokouhaki, P., Prevalence and severity of asthma symptoms in children of Tehran International Study of Asthma and Allergies in Childhood (ISAAC), Iran. J. Allergy Asthma Immunol. 3(1):25–30, 2004.Google Scholar
  10. 10.
    Innocent, P. R., and John, R. I., Computer aided fuzzy medicine diagnosis. Inf. Sci. 162:81–104, 2004.CrossRefGoogle Scholar
  11. 11.
    Zadeh, L., Fuzzy logic = computing with words. IEEE Trans. fuzzy Syst. 4(2):103–109, 1996.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Chae, Y. M., and Ho, S. H., Comparison of alternative knowledge model for diagnosis of asthma. Expert Syst. Application. 11(4):423–429, 1996.CrossRefGoogle Scholar
  13. 13.
    Lai C., Beasley R., Crane J., Foliaki S., Shah J., and Weiland S., Global Variation in the Prevalence and Severity of Asthma Symptoms: Phase Three of the International Study of Asthma and Allergic in Chilhood (ISAAC). Thorax, 2009; [Epub ahead of print].Google Scholar
  14. 14.
    Zolnoori, M., Fazel Zarandi, M.H., Moin, M., Heidarnizad, H., and Kazemnejad A., computer aided intelligence system for diagnosing pediatric asthma, journal of medical systems, [Epub ahead of print], 2010.Google Scholar
  15. 15.
    Lieu, T. A., Capra, A. M., Quesenberry, C. P., Mendoza, G. R., and Mazar, M., Computer-based models to identify high-risk adults with asthma: Is the Glass Half Empty or Half Full? J. Asthma 36(4):359–370, 1999.CrossRefGoogle Scholar
  16. 16.
    Choi, B. W., Yoo, K. H., Jeong, J. W., Yoon, H. J., Kim, S. H., Park, Y. M., Kim, W. K., Oh, J. W., Rha, Y. H., Pyun, B. Y., Chang, S. I., Moon, H. B., Kim, Y. Y., and Cho, S. H., Easy diagnosis of asthma: computer-assisted, symptom-based diagnosis. J. Korean Med. Sci. 22(5):832–838, 2007.CrossRefGoogle Scholar
  17. 17.
    Ray, P., Vervolet, D., Charpin, D., Gautier, V., Proudhin, H., Redier, H., and Godard, P. H., Evaluation of atopy through an expert system: Description of the Database. J. Clin. Exp. Allergy 25(11):1067–1073, 1995.CrossRefGoogle Scholar
  18. 18.
    Oud, M., Lung function interpolation by means of neural-network-suppor, analysis of respiration sounds. Med. Eng. Phys. 25:309–316, 2003.CrossRefGoogle Scholar
  19. 19.
    Rietveld, S., Oud, M., and Dooijes, E. H., Classification of asthmatic breath sounds: Preliminary Results of the Classifying Capacity of Human Examiners versus Artificial Neural Networks. Comput. Biomed. Res. 32:440–448, 1999.CrossRefGoogle Scholar
  20. 20.
    Uncu, U., Koklukaya, E., Gencsoy, A., and Annadurdiyew, O., A fuzzy rule-base model for classification of spirometric FVC graphs in chronical obstructive pulmonary diseases. Annu. Rep. Res. Reactor Inst. Kyoto Univ. 4:3866–3869, 2001.Google Scholar
  21. 21.
    Burge, P. S., Pantin, C. F. A., Newton, D. T., Gannon, P. F. G., Bright, P., Belcher, J., McCoach, J., Baldwin, D. R., and Burge, C. B. S. G., Development of an expert system for the interpretation of serial peak expiratory flow measurements in the diagnosis of occupational asthma. Occup. Environ. Med. 56:758–764, 1999.CrossRefGoogle Scholar
  22. 22.
    Global Initiative for Asthma, Global Strategy for Management and Prevention, National Institutes of Health, National Heart, Lung, and Blood Institute, 2007 NIH publication.Google Scholar
  23. 23.
    Redier, H., Daures, J.-P., Michel, C., Proudhon, H., Vervloet, D., Charpin, D., Marsac, J., Dusser, D., Brambilla, C., Wallaert, B., Kopferschmitt, M. C., Pauli, G., Taytard, A., Cogis, O., Cogis, O., Michel, F.-B., and Godard, P., Assessment of the severity of asthma by an expert system: Description and evaluation. Am. J. Respir. Crit. Care Med 151(21):345–352, 1995.Google Scholar
  24. 24.
    Gautier, V., Redier, H., Pujol, J. L., Bousquet, J., Proundhon, H., Michel, C., Daures, J. P., Michel, F. B., and Godard, Ph, Comparison of an expert system with other clinical scores for the evaluation of the severity of asthma. Eur. Respir. J. 9:58–54, 1996.CrossRefGoogle Scholar
  25. 25.
    Kelly, K. J., Walsh-Kelly, C. M., Barthell, E., Rogalinski, S., Christenson, P., and Grabowski, L., Analysis of pediatric asthma patient presenting to the emergency room using a web based tracking system. J. Allergy Clin. Immunol. 113(2):13–36, 2004.CrossRefGoogle Scholar
  26. 26.
    Lieut, A., Quesenberry, C. P., Sorel, M. E., Mendoza, G. R., and Leong, A. B., Computer-based models to identify high-risk children with asthma. Am. J. Respir. Crit. Care Med. 157(4):1173–1180, 1998.Google Scholar
  27. 27.
    Kim, Y. Y., Computer assisted asthma diagnosis and management program. Easy implementation into real practices. Int. J. Immunorehabil. 10(1):23–23, 2008.Google Scholar
  28. 28.
    Abramson, S. L., Shegog, R., Bartholomew, L. K., Sockrider, M., Mullen, P., Craver, J., Pilney, S., Koeppl, P., Gold, R., Czyzewski, D. I., Sellers, C., and Fernandez, M., The “Stop Asthma” clinical system: Anovel computer-based decision support program for implementation of pediatric asthma management guidelines in Houston community clinics. World Asthma Conference, Chicago, 2001.Google Scholar
  29. 29.
    Austin, T., Iliffe, S., Leaning, M., and Modell, M., A prototype computer decision support system for the asthma management. J. Med. Syst. 20(1):45–55, 1996.CrossRefGoogle Scholar
  30. 30.
    Sefion, I., Ennaji, A., Gailhardou, M., and Canu, S., ADEMA: a decision support system for asthma health care. Stud. Health Technol. Inform. 95:623–631, 2003.Google Scholar
  31. 31.
    Shegog, R., Bartholomew, L. K., Parcel, G. S., Sockrider, M. M., Mâsse, L., and Abramson, S., Impact of a computer-assisted education program on factors related to asthma self-management behavior. JAMIA 8:49–61, 2001.Google Scholar
  32. 32.
    van der Meer, V., Bakker, M. J., van den Hout, W. B., Rabe, K. F., Sterk, P. J., Kievit, J., Assendelft, W. J., and Sont, J. K., Internet-based self-management plus education compared with usual care in asthma: A randomized trial. Ann. Intern. Med. 21(2):110–120, 2009.Google Scholar
  33. 33.
    Magan, J. M., and Gerald, L. B., asthma agents: monitoring asthma in school. J. Sch. Health 76(6):300–305, 2006.CrossRefGoogle Scholar
  34. 34.
    Osman, L. M., Abdalla, M. I., Beattie, J. A. G., Ross, S. J., Russell, I. T., Friend, J. A., Legge, J. S., and Douglas, J. G., Reducing hospital admission through computer supported education for asthma patients. BMJ 308:568–571, 1994.CrossRefGoogle Scholar
  35. 35.
    Shiffman R.N., towards effective implementation of a pediatric asthma guideline: integration of decision support and clinical workflow support. Symposium on Computer Applications in Medical Care, 797–801, 1994.Google Scholar
  36. 36.
    Godard, Ph, Proudhon, H., Hibon, S., Ray, P., and Chanez, P., An expert system in asthma. Why? Who? Medical et Hygiene 55(2165):1245–1248, 1997.Google Scholar
  37. 37.
    Zolnoori M., Fazel Zarandi M.H., and Moin M., fuzzy expert system for evaluation level of asthma control, submitted in journal of electronic health informatics, December 2010.Google Scholar
  38. 38.
    Zeitz H., Lutfiyya M., McCullough J., and Henley, Use of a Web-Based Pediatric Asthma Emergency Department Tracking System to Improve Physician Asthma Care and Quality, Journal of Allergy and Clinical Immunology, 113(2):S180–S180, 2004.Google Scholar
  39. 39.
    Vollmer, M. W., O’Connor, A. E., Heumann, M., Ann, F. E., Breen, V., Villnave, J., and Buist, A. S., Searching multiple clinical information systems for longer time periods found more prevalent cases of asthma. J. Clin. Epidemiol. 57(4):392–397, 2004.CrossRefGoogle Scholar
  40. 40.
    Porter S.C., Patients as Experts: a Collaborative Performance Support System. Proc AMIA Symp. AMIA Symposium, 548–552, 2001.Google Scholar
  41. 41.
    Zeitz, H., Lutfiyya, M., McCullough, J., and Henley, E., Using Geographic Information System (GIS) software to examine US adult asthma prevalence and healthcare services disparities. J. Allergy Clin. Immunol 117(2):S180–S180, 2005.CrossRefGoogle Scholar
  42. 42.
    Porter, S. C., Cai, Z., Gribbons, W., Goldmann, D., and Kohane, I., The asthma kiosk: A Patient-centered Technology for Collaborative Decision Support in the Emergency Department. JAMIA 11:458–467, 2004.Google Scholar
  43. 43.
    Patel, A. M., Using the Internet in asthma management: Current concepts and challenges. Dis. Manage. Health Outcomes 13(5):287–293, 2005.CrossRefGoogle Scholar
  44. 44.
    Cho SH, Jeong JW, Park HW, Pyun BY, Chang SI, Moon HB, Kim YY, and Choi BW, Effectiveness of A Computer-Assisted Asthma Management Program on Physician Adherence to Guidelines. J Asthma, [Epub ahead of print], 2010.Google Scholar
  45. 45.
    Glykas, M., and Chytas, P., Technological innovations in asthma patient monitoring and care. Expert Syst. Applications. Expert Syst. Application 27(1):121–123, 2004.CrossRefGoogle Scholar
  46. 46.
    Blades, E., Kimes, D., Levine, E., Mathison, G., Thani, H., and Lavoie, M., Predicting pediatric asthma admissions for Barbados. Allergy Clin Immunol 113(2):S341–S404, 2003.Google Scholar
  47. 47.
    Bibi, M., Nutman, A., Shoseyov, D., Shalom, M., Peled, R., Kivity, S., and Nutman, J., Prediction of emergency department visits for respiratory symptoms using an artificial neural network. Chest 122(5):1627–1632, 2002.CrossRefGoogle Scholar
  48. 48.
    Computer monitors wheezing in asthma patients, science daily, www. http://www.sciencedaily.com/releases/2001/07/010726103637.htm, 2001.
  49. 49.
    Finkelstein, J., and Gangopadhyay, A., Using machine learning to predict asthma exacerbations. AMIA Annu. Symp. Proc. 11:955, 2007.Google Scholar
  50. 50.
    Lee CH, Chen JC, and Tseng VS., A novel data mining mechanism considering bio-signal and environmental data with applications on asthma monitoring, Comput Methods Programs Biomed., [Epub ahead of print], 2010Google Scholar
  51. 51.
    Dexheimer, J. W., Brown, L. E., Leegon, J., and Aronsky, D., Comparing decision support methodologies for identifying asthma exacerbations. Stud. Health Technol. Inform. 129(Pt 2):880–884, 2007.Google Scholar
  52. 52.
    Intchhaporia, D., Snow, P. B., Alamassy, R. J., and Oetgen, W. J., artificial neural networks: current status in cardiovascular Medicine. JACC 28(2):515–521, 1996.Google Scholar
  53. 53.
    Aki A.I., sobh M.A., Enab Y.M., and Tattersall J., Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy. American journal of kidney disease, 38 (6), 1277–1283, 2001.Google Scholar
  54. 54.
    Buchanan, B. G., Moore, J. D., Forsythe, D. E., Carenini, G., Ohlsson, S., and Banks, G., An intelligent interactive system for delivering individual information to patients. Artif. Intell. Med. 7:117–154, 1995.CrossRefGoogle Scholar
  55. 55.
    Beliakov, G., and Warren, J., Fuzzy logic for chronic care. Artif. Intell. Med. 21(1–3):209–213, 2001.CrossRefGoogle Scholar
  56. 56.
    Lin, R. H., An intelligent model for liver disease diagnosis. Artif. Intell. Med. 47:53–62, 2009.CrossRefGoogle Scholar
  57. 57.
    Smith, S. L., and Timmis, J., An immune network inspired evolutionary algorithm for diagnosing of Parkinson’s disease. Biosystems 94:34–46, 2008.CrossRefGoogle Scholar
  58. 58.
    Ramoni, M., Riva, A., Stefanelli, M., and Patel, V., an ignorant belief network to forcast glucose concentration from clinical databases. Artif. Intell. Med. 7(6):541–559, 1995.CrossRefGoogle Scholar
  59. 59.
    Rees, J., Asthma control in adults. BMJ 332:767–771, 2006.CrossRefGoogle Scholar
  60. 60.
    Lemanske, R. F., and Busse, W. W., Asthma: factors underlying inception, exacerbation, and disease progression. J. Allergy Clin. Immunol. 117:S456–S461, 2006.CrossRefGoogle Scholar
  61. 61.
    Morell, F., Genover, T., Muñoz, X., García-Aymerich, J., Ferrer, J., and Cruz, M.-J., Rate and characteristic of asthma Exacerbations: The ASMAB I study. Arch. Bronconeumol. 44(6):303–311, 2008.CrossRefGoogle Scholar
  62. 62.
    Phuong, H. N., and Kreinovich, V., Fuzzy logic and its applications in medicine. Int. J. Med. Inform. 62:165–173, 2001.CrossRefGoogle Scholar
  63. 63.
    Emami, R. M., Turksen, I. B., and Goldenberg, A. A., A unified parameterized formulation of reasoning in fuzzy modeling and control. Fuzzy Sets Syst. 108:59–81, 1999.MathSciNetMATHCrossRefGoogle Scholar
  64. 64.
    H.T., Chu, C.C Huang, Z.H. Lian, J.J.P., and Tsai, A ubiquitous warning system for asthma-inducement. IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing - Vol 2, 2006Google Scholar
  65. 65.
    Durkin, J., Expert systems: design and development. Macmillan publisher, UK, 1994.MATHGoogle Scholar
  66. 66.
    Pedrycz, W., Why triangular membership functions? Fuzzy Sets Syst. 64:21–30, 1994.MathSciNetCrossRefGoogle Scholar
  67. 67.
    Fazel Zaranid M.H., Zolnoori, M. Moin M., and Heidarnejad H., a fuzzy rule-based expert system for diagnosing asthma, Journal of Scientia Irania: Transaction E, industrial engineering, 17(2), 2010.Google Scholar
  68. 68.
    Cohen’s kappa,http://en.wikipedia.org/wiki/Cohen’s_kappa, Date accessed: December 2010

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Maryam Zolnoori
    • 1
    • 2
  • Mohammad Hossein Fazel Zarandi
    • 3
  • Mostafa Moin
    • 4
  1. 1.Mathematic and Informatics Group, Academic Center for Education, Culture and Research (ACECR)Tarbiat Modares UniversityTehranIran
  2. 2.Department of Information Technology ManagementTarbiat Modares UniversityTehranIran
  3. 3.Department of Industrial EngineeringAmirkabir University of TechnologyTehranIran
  4. 4.Immunology, Asthma and Allergy Research InstituteTehran University of Medical SciencesTehranIran

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