ACİL SERVİSLER İÇİN BULANIK MANTIK TABANLI BİR KLİNİK KARAR DESTEK SİSTEMİ

Yıl 2018, , 375 - 382, 28.09.2018

Osman Özkaraca , Ethem Acar , Musa Peker , Erdem Türk

https://doi.org/10.21923/jesd.378742

Cited By: 2

Öz

Acil servisler, her hastanede olan ve içerisinde özel birimlerin bulunduğu, birçok problemi olan en önemli birimlerinden biridir. Bu sorunların başında, acil servislerin kalabalık olması ve acil hasta bakım planlamasının zorluğu gelmektedir. Bu problemler için triyaj sistemi gibi uygulamalar kullanılmaktadır. Fakat bu gibi uygulamalarında problemlere tam olarak çözüm getiremediği bilinmektedir. Bu çalışmada acil servise gelen hastaların sınıflandırılmasına yönelik bulanık mantık tabanlı bir klinik karar destek sistemi (KKDS) gerçekleştirilmiştir. Çalışmada Muğla Sıtkı Koçman Üniversitesi Eğitim ve Araştırma Hastanesi'nde anonim olmayan 180 hastanın başvuru şikâyetleri ve medikal verileri kullanılmıştır. Hastaların 95'i kadın, 85'i erkek olup yaş ortalamaları 46’dır. Gerçekleştirilen sistemin performansını test etmek için uygulamanın sonuçları ve uzman hekimin kararları istatistiksel olarak değerlendirilerek (doğruluk, duyarlılık ve özgüllük) karşılaştırılmıştır. Sonuç olarak, gerçekleştirilen sistemin doğruluğu %83, duyarlılığı %87, özgüllüğü %76,6 bulunmuştur. En son kararın uzman hekime ait olması şartıyla bu tür KKDS’nin geliştirilmesi hastanelerin acil servislerinde özellikle yoğun olduğu dönemlerde ciddi zaman ve mekân açısından kazançlı olacağı düşünülmektedir.

Anahtar Kelimeler

Acil tıp, Karar destek sistemi, Bulanık mantık

A FUZZY LOGIC BASED CLINICAL DECISION SUPPORT SYSTEM FOR EMERGENCY SERVICES

Öz

Emergency departments are one of the most important units in the hospital where there are special units and many problems. At the beginning of these problems, emergency services are crowded and urgent patient care planning is difficult. The applications such as triage system are used for these problems. However it is known that such applications do not fully solve these problems. In this study, a fuzzy logic based clinical decision support system (CDSS) was developed for the classification of emergency patients. In the study, application complaints and medical data of 180 non-anonymous patients in Muğla Sıtkı Koçman University Training and Research Hospital were used. The 95 of the patients are female, 85 are male and the average age is 46. In order to analysis the performance of the performed system, the results of the application and the decisions of the specialist doctor were compared statistically (accuracy, sensitivity and specificity). Consequently, the accuracy of the realized system 83%, sensitivity 87% and specificity 76.6% was found. Provided that the most recent decision belongs to the expert physician, the development of this kind of CDSS is thought to be beneficial in terms of serious time and space in the emergency departments of the hospitals, especially during intensive periods.

Anahtar Kelimeler

Emergency medicine, Decision support system, Fuzzy logic

Kaynakça

• Anooj, PK., 2012. Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University –Computer and Information Sciences, 24: 27-40.
• Augustyn, J., Hattingh, S., Ehlers, V., 2007. Implementing a triage system in an emergency unit: a literature review. Afr J Nurs Midwifery, 9: 12 – 33.
• Baratloo, A., Hosseini, M., Negida, A., El Ashal, G., 2015. Part 1: Simple Definition and Calculation of Accuracy, Sensitivity and Specificity. Emergency, 3:48-49.
• Bryan, LA., Bryan, EA., 1997. Programmable Controllers Theory and Implementation. 2nd ed. Atlanta: Industrial Text Company.
• Duran, A., Sit, M., Ocak, T., 2013. Effect of density in emergency services on waiting time. South Eastern Europe Health Sciences Journal, 3:32-7.
• Erdem, N., 2011. Specialty Thesis in Medicine: Acil Servise Başvuran Dahili Grup Hastaların Değerlendirilmesinde ve Kritik Hasta Seçiminde Skorlama Sistemlerinin Rolü. Istanbul Bilim University, İstanbul, Turkey.
• Gholami, B., Bailey, JM., Haddad, WM., Tannenbaum, AR., 2012. Clinical Decision Support and Closed-Loop Control for Cardiopulmonary Management and Intensive Care Unit Sedation Using Expert Systems. IEEE Trans Control Syst Technol, 20: 1343-50.
• Internet-1 Mace, SE., Mayer, TA. Triage. http://www.us.elsevierhealth.com/media/us/samplechapters/ 9781416000877/Chapter%20155.pdf.
• Internet-2 Fuzzy Logic Fundementals. http://ptgmedia.pearsoncmg.com/images/0135705991/samplechapter/ 0135705991.pdf.
• Internet-3 Yıldız, ZC., A Short Fuzzy Logic Tutorial. http://cs.bilkent.edu.tr/~zeynep/files/short_fuzzy_logic_ tutorial.pdf.
• Internet-4 Zhu, W., Zeng, N., Wang, N., Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS Implementations. http://www.nesug.org/Proceedings/nesug10/hl/hl07.pdf.
• Internet- 5 United States Government Accountability Office. Hospital emergency departments; crowding continues. http://www.gao.gov/new.items/d09347.pdf.
• McHugh, M., Tanabe, P., McClelland, M., Khare, RK., 2012. More patients are triaged using the Emergency Severity Index than any other triage acuity system in the United States. Acad Emerg Med., 19:106-109.
• Mendel, JM., 1995. Fuzzy logic systems for engineering: A tutorial. Proceedings of the IEEE, 83:345 – 377.
• Mirhaghi, A., Kooshiar, H., Esmaeili, H., Ebrahimi, M., 2015. Outcomes for Emergency Severity Index Triage Implementation in the Emergency Department. J Clin Diagn Res, 9: OC04-OC07.
• Ozüçelik, DN., Kunt, MM., Karaca, MA., et al., 2013. A model of complaint based for overcrowding emergency department: Five-Level Hacettepe Emergency Triage System. Ulus Travma Acil Cerrahi Derg, 19: 205-14.
• Sivanandam, SN., Sumathi, S., Deepa, SN., 2007. Introduction to Fuzzy Logic using MATLAB. New York: Springer, 2007.
• Somma, SD., Paladino, L., Vaughan, L., et al., 2015. Overcrowding in emergency department: an international issue. Intern Emerg Med, 10: 171-75.
• Sperandio, F., Gomes, C., Borges, J., Carvalho Brito, A., Almada-Lobo, B., 2014. An Intelligent Decision Support System for the Operating Theater: A Case Study. IEEE Trans Autom Sci Eng, 11: 265-273.
• Torres, A., Nieto, JJ., 2006. Fuzzy Logic in Medicine and Bioinformatics. J Biomed Biotechnol, doi:10.1155/JBB/2006/91908.
• Van der Linden, C., Lindeboom, R., Van der Linden, N., Lucas, C., 2011. Refining a triage system for use in emergency departments. Emerg Nurse, 19:22-4.
• Wicht, A., Wetter, T., Klein, U., 2013. A web-based system for clinical decision support and knowledge maintenance for deterioration monitoring of hemato-oncological patients. Comput Methods Programs Biomed, 111: 26-32.
• Zadeh, L., 1965. Fuzzy sets. Information and Control, 8:338–353.