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1- PhD candidate in Medical Informatics, Health Information Management Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
2- Assistant Professor, Health Information Management Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran , Langarizadeh.m@iums.ac.ir
3- Master of Sciense in Medical Informatics, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
4- Ph.D. Candidate in Electrical and Computer Engineering, Department of Computer, University of Ontario Institute of Technology (UOIT), Ontario, Canada
5- Associate Professor,Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
6- PhD candidate in medical informatics, Health Information Management Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Abstract:   (113 Views)
Background and Aim: Artificial intelligence is a branch of computer science that has the ability of analyzing of complex medical data. Using of artificial intelligence is common in diagnosing, treating and caring of patients. Warfarin is one of the most commonly prescribed oral anticoagulant. Determine the exact dose of warfarin needed for patients is one of the major challenges in the health system which has attracted the attention of researchers. The purpose of this study was to determine exact dose of warfarin needed for patients with artificial heart valve using artificial neural networks (ANN).
Materials and methods: A total of 9 multi-layer perceptron ANN were constructed with different structures and evaluated based on a dataset including 846 patients who had been referred to the PT clinic in Tehran heart center in the second half of the year 2013. Finally, the best structure of ANN for warfarin dose was investigated. All simulations included data preprocessing and neural network implementation were done in the MatLab environment.
Results: The effectiveness of ANNs were evaluated in terms of classification performance using 10fold cross-validation procedure and the results showed that the best model is a network that has 7 neurons in its hidden layer with an average absolute error of 0.1, disturbance rate of 0.33 and regression of 0.87. 
Conclusion: The achieved results reveal that ANNs are able to predict warfarin dose in Iranian patients with an artificial heart valve. However, no system can be guaranteed to achieve 100% accuracy, but such systems can be effective in reducing medical errors.
     
Type of Study: Research | Subject: Health Information Technology
ePublished: 2018/10/16

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