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Designing Decision Support System to Detect Drug Interactions Type 2 Diabetes

Mehdi Rasoolimoghadam, Reza Safdari, Marjan Ghazisaeidi, MohammadReza Maharanitehrani, Shahram Tahmasebiyan.


Background and Aim: Type II Diabetes is the most common diseases of metabolic disorders and the treatment of oral anti-diabetic drug use takes place But The problem of using multi-drug and interactions at the same time is an issue that has always been a major challenge And diagnosis of drug interactions, particularly in Diabetic patients due to the problem with the disease is very important. The purpose of this studying is, to design a clinical assistant decided to use this approach to determine the type II diabetes drug interactions this makes it easy for those who are active in the field. Materials and Methods: Study is Developmental that to determine the content of the system a self-made checklist was used. Checklist Validity and reliability has been confirmed by four professors. The Research community to determine the content of the system was country endocrine that are 124 people.  The sample size was calculated using Cochran that was 57 people. The Score of checklist was calculated in SPSS version 20 .finally, the checklist was approved by at least 70% points. The system by using Microsoft SQL server 2008 and visual Studio 2012 development environment was designed in C#.net. In the end, In order to evaluate the software to determine the level of satisfaction, usability and ease of use, designed systems sharing with all Medical Informatics students of Tehran University of Medical Sciences. For this purpose a self-made questionnaire was used. Questionnaire Validity has been confirmed by four professors and reliability was assessed by Cronbach method. Results: The results of the survey are showing that the majority of students found out and believed the software is useful and easy to use and generally expressed their satisfaction software. Conclusions: The methodology provides a suitable approach for analysis and modeling of data in the medical field and the performance is good.

Key words: Decision support system, Type 2 diabetes, Drug interactions.

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