Expert system in medicine and its application on pulmonary diseases

Yıl 2016, Cilt: 3 Sayı: 11, 342 - 349, 30.11.2016

Evren Bursuk , Sabriye Demirci Mehmet Ali Korpinar

https://doi.org/10.17546/msd.273051

Öz

Objective: Our aim is to develop a medical expert system for pulmonary diseases providing practitioners and medical students with the advantages of improving their ability, minimizing the error and cost in diagnosing and developing their medical knowledge. Material and Methods: CLIPS  has been chosen as a programming environment for this study. A respiratory disease binary decision tree which helps us to create the system database which includes twenty-eight diseases is formed for the inference engine of this program. Results: The evaluation of this program is based on hundred and eighty-nine patients’ data each is classified into three data types. These are patient’s history and physical findings, radiological data and laboratory data. The combination of them shapes four different data sets for each patient. The diagnosing result for each data set of each patient is compared with diagnosing of gold standards. If both results indicates the same disease this operation of the program is assumed as “accurate”, otherwise as “error”. These operations for the considered each data set are repeated for all patients’ data. The total number of accurate diagnosis is divided by the number of all patients and these accuracy rates are respectively 64.02%, 71.43%, 82.54% and 96.83%. Conclusion: We can conclude that the accuracy of the system is enhanced with the increasing total number and type of data for each patient. Finally, further improvement on the performance and accuracy of the system may be obtained by designing the program with the self-learning ability.

Anahtar Kelimeler

expert system, Pulmonary diseases, Artificial intelligence, Health

Kaynakça

• 1. Barr A, Feigenbaum EA, editors. The Handbook of Artificial Intelligence. Stanford, California: Heuris Tech Press; 1982, Vol.2.
• 2. Fox J, Alvey P. Computer Assisted Medical Decision Making. BMJ 1983; 287: 742-746.
• 3. Giarratano J, Riley G, editors. Expert Systems. 2nd ed. Boston: PWS Pub Com; 1994.
• 4. Barnett GO. Dxplain an evolving diagnostic decision support system. JAMA 1987; 258: 67-71.
• 5. Compton P, Jansen R. Diabetes expert systems: planning for long term use. Hormone and Metabolic Research Supplement Series 1990; 24: 124-129.
• 6. Forsstrom J, Nuutila P, Irjala K. Using the ID3 algorithm to find discrepant diagnoses from laboratory databases of thyroid patients. Medical Decision Making 1991; 11: 171-175.
• 7. Verdaguer A. Validation of the Medical Expert System PNEUMON-IA. Computers and Biomedical Research 1992; 25: 511-526.
• 8. Hernandez C. Validation of the medical expert system RENOIR. Computers and Biomedical Research 1994; 27: 456-471.
• 9. Birndorf NJ. An expert system to diagnose anemia and report results directly on hematology forms. Computers and Biomedical Research 1996; 29: 16-26.
• 10. Thornett AM. Computer decision support systems in general practice. International Journal of Information Management 2001; 21: 39-47.
• 11. Zini G. Artificial intelligence in hematology. Hematology 2005; 10(5): 393-400.
• 12. Schectman JM, Schorling JB, Nadkarni MM, Voss JD. Determinants of physician use of an ambulatory prescription expert system. International Journal of Medical Informatics 2005;74: 711-717.
• 13. Razzouk D, Mari JJ, Shirakawa I, Wainer J, Sigulem D. Decision support systems for the diagnosis of schizophrenic disorders. Brazilian journal of Biology Research 2006; 39 (1): 119-128.
• 14. Naser SSA, Ola AZA. An expert system for diagnosing eye diseases using CLIPS. Journal of Theoretical and Applied Information Technology 2008; 4(10):923-930.
• 15. Naser SSA, Dahdooh RA, Mushtaha A, Naffar ME. Knowledge management in ESMDA: expert system for medical diagnostic assistance. ICGST- AIML Journal 2010 Oct; 10(1):31-40.
• 16. Naser SSA, Hissi HE, Rass MA, Khozondar NE. An expert system for endocrine diagnostic and treatments using JESS. Journal of Artificial Intelligence 2010; 3(4):239-251.
• 17. Winstanley T, Courvalin P. Expert systems in clinical microbiology. Clinical Microbiology Reviews 2011 July; 24(3); 515-556.
• 18. Nohria R. Medical expert system-A comprehensive review. International Journal of Computer Applications 2015 Nov; 130(7): 44-50.
• 19. Bahrami A, Roozitalab N, Jafari S, Bahrami A. An expert system for diagnosing dilated cardiomyopathy. International Journal of Engineering Science Invention 2014 Mar; 3(3):38-42.
• 20. Singla J, Grover D, Bhandari A. Medical expert systems for diagnosis of various diseases. International Journal of Computer Applications 2014 Mar; 93(7): 36-43.
• 21. Bergman DA, Pantell RH. The art and science of medical decision making. Journal of Pediatrics 1984; 104 (5): 649-656.
• 22. Elstein AS, Bordage G. Psychology of clinical reasoning. In: Dowie J, Elstein AS, editors. Professional Judgement a reader in clinical decision making. 6th ed. Cambridge: Cambridge University Press; 1999. P. 109-130.
• 23. Elstein AS, Friedman CP, Wolf FM, Murphy G, Miller J, Fine P, et al. Effects of a decision support system on the diagnostic accuracy of users: A preliminary report. Journal of The American Medical Informatics Association 1996; 3 (6): 422-428.
• 24. File PE, Dugard PI, Houston AS. Evaluation of the use of induction in the development of a medical expert system. Computers and Biomedical Research 1994; 27: 383-395.
• 25. Hunt DL, Haynes RB, Hanna SE. Effects of Computer-based clinical decision support systems on physician performance and patient outcomes, A systematic review. JAMA 1998; 280: 1339-1346.
• 26. Pankaskie MC, Wagner MM. Use of CLIPS for representation and inference in a clinical event monitor. In: Masys DR, editor. Proc AMIA Annu Fall Symp 1997. Proceedings of American Medical Informatics Association Annual Fall Symposium; 1997 Oct 25-29; Nashville, Tennessee, United States of America. Oxford; 1997. P. 193-7.
• 27. Camilli A. Hemoptysis. In: Greene HL, et. al, editors. Decision Making in Medicine An Algorithmic Approach. 2nd ed. St. Louis: Mosby; 1998. P. 388-389.
• 28. Camilli A. Pulmonary Dyspnea. In: Greene HL, et. al, editors. Decision Making in Medicine An Algorithmic Approach. 2nd ed. St. Louis: Mosby; 1998. P.396-397.
• 29. Clements NC. Wheezing. In: Greene HL, et. al, editors. Decision Making in Medicine An Algorithmic Approach. 2nd ed. St. Louis: Mosby; 1998. P. 392-393.
• 30. Drazen JM, Weinberger SE. Disorders of the respiratory system. In: Fauci AS, et al, editors. Harrison’s Principles of Internal Medicine. 14th ed. New York: McGraw-Hill; 1998. P. 1407-1493.
• 31. Knoper SR. Cough. In: Greene HL, et. al, editors. Decision Making in Medicine An Algorithmic Approach. 2nd ed. St. Louis: Mosby; 1998. P. 388-389.
• 32. Murray JF. Respiratory Diseases. In: Wyngaarden JB, et al, editors. Cecil Textbook of Medicine. 19th ed. Philadelphia: W.B. Saunders Company; 1992. P. 369-458.
• 33. Stauffer JL. Lung. In: Tierney LM, et. Al, editors. Current Medical Diagnosis and Treatment. 36th ed. Stamford: Appleton & Lange; 1997. P. 236-319.
• 34. Wyatt J. Quantitative evaluation of clinical software, exemplified by decision support systems. International Journal of Medical Informatics 1997; 47: 165-173.