A Medical Expert System for Prostate Diseases

Topics: Benign prostatic hyperplasia, Prostate, Fuzzy logic Pages: 9 (2987 words) Published: March 15, 2013
HIROFILOS: A Medical Expert System for Prostate Diseases
CONSTANTINOS KOUTSOJANNIS 1, MARIA TSIMARA2, EMAN NABIL1 1 Medical Physics Lab, Department of Physiotherapy TEI of Patras Psaron 6, Aigion 2 Department of Medicine University of Patras Rion, Patras GREECE ckoutsog@teipat.gr, http://aigio.teipat.gr Abstract: - Prostate gland diseases, including cancer are estimated to be one of the leading cause of male death worldwide and its management is based on guidelines regarding diagnosis, evaluation, treatment and continuing care. In this study a fuzzy expert system for diagnosing, and learning purpose of the prostate diseases is described. HIROFILOS is a fuzzy expert system for diagnosis and treatment of prostate diseases according to symptoms that are realized in one patient and usually recorded through his clinical examination as well as specific test results. The user-friendly proposed intelligent system is accommodated on a hospital web page for use as a decision support system for resident doctors, as an educational tool for medical students, as well as, an introductory advisory tool for interested patients. It is based on knowledge representation provided from urology experts in combination with rich bibliographic search and study ratified with statistical results from clinical practice. Preliminary experimental results on a real patient hospital database provide an acceptable performance that can be improved using more than one computational intelligence approaches in the future. Key-Words: - Prostate disease, , urology, medical expert system, fuzzy logic.

1 Introduction
Prostate gland diseases, including cancer are estimated to be one of the leading cause of male death worldwide and its management is based on guidelines regarding diagnosis, evaluation, treatment and continuing care. Prostate cancer is the most common noncutaneous cancer among males [1]. The diagnosis and treatment of prostate cancer continue to evolve. With the development of prostate-specific antigen (PSA) screening, more men are identified earlier as having prostate cancer. While prostate cancer can be a slow-growing cancer, thousands of men die of the disease each year. Benign prostatic hyperplasia (BPH) is a noncancerous enlargement of the prostate gland that may restrict the flow of urine from the bladder. BPH involves both the stromal and epithelial elements of the prostate arising in the periurethral and transition zones of the gland; the condition is considered a normal part of the aging process in men and is hormonally dependent on testosterone production. An estimated 50% of men demonstrate histopathologic BPH by age 60 years. This number increases to 90% by age 85 years; thus, increasing gland size is considered a normal part of the aging process. Acute prostatitis (AP) presents as

an acute urinary tract infection in men. It is much less common than chronic prostatitis (CP)but is easier to identify because of its more uniform clinical presentation. Chronic prostatitis, is poorly understood partly because of its uncertain etiology and lack of clearly distinguishing clinical features. Acute prostatitis is usually associated with predisposing risk factors, including bladder outlet obstruction secondary to benign prostatic hyperplasia (BPH) [1]. Different approaches according to medical as well as psychosocial characteristics of patients are usually followed for diagnosis of the previous diseases. Like any chronic disease, prostate is complex to manage. Traditionally, an intelligent system that helps clinicians to diagnose and treat diseases is used to identify a patient-specific clinical situation on the basis of key elements of clinical and laboratory examinations and consequently usually refine a theoretical treatment strategy, a priori established in the guideline for the corresponding clinical situation, by the specific therapeutic history of the patient [2]. Depending on the patient's data, it models patient scenarios which drive...

References: [1] European Association of Urology, Guidelines 2007, http://www.uroweb.org/professionalresources/guidelines/. [2] Pereira M, Schaefer M, B Marques J. Remote expert system of support the prostate cancer diagnosis. Conf Proc IEEE Eng Med Biol Soc. 2004;5:3412-5. Llobet R, Pérez-Cortés [3] C, Toselli AH, Juan A. Computer-aided detection of prostate cancer. Int J Med Inform. 2007 Jul;76(7):547-56. Epub 2006 Apr 18. [4] Chang PL, Wang TM, Huang ST, Hsieh ML, Tsui KH, Lai RH. Use of a medical decision support system to improve the preoperative diagnosis of prostate cancer with pelvic lymph node metastases. Changgeng Yi Xue Za Zhi. 1999 Dec;22(4):556-64. [5] Chang PL, Li YC, Wang TM, Huang ST, Hsieh ML, Tsui KH. Evaluation of a decision-support system for preoperative staging of prostate cancer. Med Decis Making. 1999 OctDec;19(4):419-27. [6] Anagnostou T, Remzi M, Lykourinas M, Djavan B. Artificial neural networks for decision-making in urologic oncology. Eur Urol. 2003 Jun;43(6):596-603. [7] Bagli DJ, Agarwal SK, Venkateswaran S, Shuckett B, Khoury AE, Merguerian PA, McLorie GA, Liu K, Niederberger CS. Artificial
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