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İLAÇ İLAÇ ETKİLEŞİMLERİNİN JORDAN ELMAN AĞLARI KULLANILARAK SINIFLANDIRILMASI

CLASSIFICATION OF DRUG DRUG INTERACTIONS USING JORDAN ELMAN NETWORKS

Journal Name:

  • Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

Publication Year:

  • 2014

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Usage of drug has many risks. These risks are drug related problems in hospital admission, drug related problems during hospitalization, drug related problems at hospital discharge, medication errors and drug-drug interactions (DDIs) [1]. Because of the DDIs fatal effects, U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMEA) are researching at this area [2]. Lazarou et al. investigated 6.7% of hospitalized patients, having a fatal DDIs with the rate of 0.32% [3]. The cost of DDIs related mortality is $136 billion annually in the USA [4]. Preventing the deadly effects of DDIs, classifying of DDIs using Neural Networks is aimed at this study. In this study, Jordan Elman Networks were applied for some DDIs and classification process trained for 1000 steps. At the end of 149 training step, using Levenberg Marquardt learning algorithm, Jordan network has been constituted with 0.0305 MSE and as a result of testing network, correlation coefficient was obtained as 0.8177. This study is also supported by Republic of Turkey Ministry of Science, Industry and Technology as “Drug Interaction (code number is 00912.STZ.2011-1)”
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Abstract (Original Language): 
İlaç kullanımı birçok risk içermektedir. Bu riskler hastanede yatış için kabul alanların aldıkları ilaçla ilgili problemler, hastanede yatış sırasında alınan ilaca bağlı sorunlar, taburcu edilmeden alınan ilaç problemleri, medikal hatalar ve ilaç ilaç etkileşimleri(İİE)dir [1]. İİE’lerin ölümcül etkilerinden dolayı, FDA (U.S. Food and Drug Administration) ve EMEA (European Medicines Agency) bu alanda çalışmalar yapmaktadır [2]. Lazarou ve arkadaşlarına göre hastanede yatan hastaların %6,7’sinde, %0,32’lik bir oranda ölümcül İİE tespit edilmiştir [3]. İİE’den dolayı ölümlerin ABD’ye maliyeti yıllık 136milyar$ olmaktadır [4]. İİE’lerin ölümcül etkilerinin önüne geçilmesi için bu çalışmada İİE’lerin yapay sinir ağlarıyla sınıflandırılması amaçlanmaktadır. Bu çalışmada Jordan Elman Ağları bazı İİE’lere uygulanmış ve sınıflandırma işlemi 1000 adımda eğitilmiştir. Eğitimin 149 adım sonunda Levenberg Marquardt öğrenme algortimasıyla 0,0305’lik bir MSE ile Jordan ağı oluşturulmuş ve ağın test sonucu 0,8177’lik korelasyon katsıyısı elde edilmiştir. Bu çalışma ayrıca Türkiye Cumhuriyeti Bilim Sanayi ve Teknolji Bakanlığı tarafından 00912.STZ.2011-1 kod numaralı “İlaç Etkileşimleri” projesi olarak desteklenmiştir.
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  • Turkish

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Şekil 6. Elman’s Ağı İçin Beklenen ve Gerçek Ağ Çıkışları (Elman Network’s Desired and Network Output)
Şekil 7. Jordan Ağı İçin Beklenen ve Gerçek Ağ Çıkışları (Jordan Network’s Desired and Network Output)
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