Journal of

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Background and Aim: Development of information and communication technology has led to enormous changes in different areas. Electronic medical records system is valuable to access patient data in hospitals. This study aimed to investigate and compare the educational hospitals of Uemia University of Medical Sciences in case of technical, organizational and legal to establish the system.

Materials and Methods: The study was a descriptive cross-sectional study. The study population consisted of 98 senior and central managers. In this study population census was used and the entire population were considered as the sample. A questionnaire was used for data collection, which included two sections in order to determine the level of research community awareness and to analyze the standards related requirements for the implementation of the system. Validity and reliability were assessed and the data was analyzed by SPSS.

Results: Sample awareness in 5 hospitals of this study was moderate. In terms of requirements, there was a significant difference between the means of Electronic Medical Records in terms of three variables between hospitals "D" and the rest of the hospitals. And no significant difference was seen among other hospitals.

Conclusion : Three hospitals, "a", "c" and "b", among five studied hospitals are in preparation for the deployment of electronic medical records. Other two hospitals were not prepared. However, the implementation of electronic medical records, increases health care quality, patient safety and patient care and also decreases health costs. So it is suggested that hospitals do necessary efforts to establish EMR.

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 Background and Aim: The risk of breast cancer increases directly in line with breast density. Therefore, it is important to pay more attention to denser breasts in order to detect abnormalities. The aim of this paper was to design and suggest a quantitative method to categorize breast density in digital mammogram images using fuzzy logic.

 Materials and Methods: This was a crosectional study which resulted in developing a new system. The research population was including patients who undergo mammography in National Cancer Society of Malaysia during 2010 to 2011. Sample included 220 mammogram images which was selected randomly. Data analysis was done using SPSS with Kappa statistics.

 Results: Accuracy level of 92.8% was obtained based on evaluation of the system and there was a strong correlation between the system output and radiologists’ estimation (K=0.87, p=0.0001).

 Conclusion : Results obtained from the suggested system had higher performance than similar systems. Therefore, it could be concluded that the fuzzy logic may be used in this area. In addition, such systems could be helpful for physicians.

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Background and Aim: Tele pathology is one of the medical subdivisions that has opened a new approach in the telepathology, e specially to organize consultations. In this research, feasibility of Telepathology implementation in teaching hospitals of Tehran University of Medical Science was studied.

Materials and Methods: This study was a cross-sectional and descriptive study. The study population was included 8 hospitals directors and administrator, 20 pathologists, and 8 informatics staffs, in four teaching hospitals of Tehran University of Medical Sciences. A researcher constructed questionnaire was used for data collection . The validity of the questionnaire was confirmed by expert panel and using by Test – retest method confirmed its reliability. The data was collected and analyzed by SPSS software to prepare descriptive findings.

Results: The R esults showed that 65.6% of hospitals had hardware facilities . Procedures based on legal issues related to information security and privacy was 95.71%, while t here was no guideline for telemedicine and telepathology.

Conclusion: I t could be concluded that in line with considrating the importance and benefits of telepathology, it is necessary to provide software requirements and hardware infrastructure. It should be noted that available properties also must be improved in terms of implementation of telepathology. Also, rules to support patients’ and staff’s rights should be developed for better implementation of such new technologies

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Background and Aim: Data mining is a very important branch in deeper understanding of medical data, which attempts to solve problems in the diagnosis and treatment of diseases. One of the most important data mining applications is to examine the existing data patterns. The present study aims to examine the existing data patterns of patients with asthma. Materials and Methods: This study was performed on 258 patients with respiratory symptoms, who referred to Imam Khomeini and Masih Daneshvari Hospitals in 2009. All records were entered into Excel software, and data mining add-ins were used. Analyses such as key influencers, cluster analysis of patients, and detecting exceptions have been done. Results: The most common clinical sign of asthma among subjects was severe coughing, which was highly affected by thrills. The data were aggregated into 5 clusters for more general analyses. Their common denominator was then identified and the records with exceptional features were determined. Then, following cost analysis and setting the threshold value at 612, a questionnaire was developed based on data features for diagnosis of asthma. Conclusion: The developed framework for data mining and analysis is an appropriate tool for knowledge extraction based on the data and their relationships. Meanwhile, it can identify and fill the existing gap in medical decision- making when using clinical guideline

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Background and Aim: Diagnosis of preeclampsia has an essential role in applying appropriate treatment plan for the patients. The aim of this study was to design an expert system in order to diagnos preeclampsia in order to assist the clinicians.

Materials and Methods: This was a cross-sectional study which resulted in developing a new system. The study population consisted of all patients admitted to three Maternity hospitals affliated to Tehran University of Medical Sciences (TUMS). Sample size included 215 medical records which were randomly selected. The results obtained were compared with the diagnosis from experts by kappa test using SPSS software.

Results: First of all, input parameters fuzzificated and entered into inference engine. Outputs were categorized in two groups as patients and healthy, with the final diagnosis and clinical explanation. The results obtained from system evaluation showed that accuracy, specificity and sensitivity of the system were 98.2 percent, 100 percent and 96.4 percent respectively.

Conclusion: Based on evaluation results, it could be concluded that fuzzy logic is an efficient method for designing of expert systems in the field of obstetrics and gynecology. Also, due to the similarity of the logic used in the proposed system with workflow and medical decision making, it will be accepted by the physicians.

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Background and Aim: Bacterial meningitis detection is a complicated problem because of having several components in order to be diagnosed and distinguished from other types of meningitis. Fuzzy logic and neural network, frequently used in expert systems, are able to distinguish such diseases. The purpose of this paper is to compare Fuzzy logic and artificial neural networks for distinguishing bacterial meningitis from other types of meningitis.
Materials and Methods: In this study to detect and distinguish bacterial meningitis from other types of meningitis, in the first step 6 attributes were selected by infectious disease specialists. In the second step, systems were designed by Matlab software. The systems were evaluated by 26 records of meningitis patients, and results were analyzed by SPSS software.
Results: The evaluation showed that the accuracy, specificity and sensitivity of fuzzy method were 88%, 92% and 100% respectively and those of neural network methods were 92%, 94% and 88% respectively. The Kappa test result in fuzzy and neural network methods were 0.83 (p<0.001) and 0.83 (p<0.001). The areas under the ROC curves were 0.94 and 0.91 respectively.
Conclusion: The sensitivity, the Kappa test results and the areas under the ROC curve of the fuzzy logic method were better than neural network method. However the fuzzy logic method is more reliable to distinguish bacterial meningitis from other type of Meningitis, the evaluation result were obtained from 26 records of meningitis patient which were hospitalized in the same center leads to the study be still open.

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Background and Aim: Artificial intelligence is a branch of computer science that has the ability of analyzing complex medical data. Using artificial intelligence is common in diagnosing, treating and taking care of patients. Warfarin is one of the most commonly prescribed oral anticoagulants. Determining 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 the exact dose of warfarin needed for patients with artificial heart valves using artificial neural networks (ANN).
Materials and Methods: A total of 9 multi-layer perceptron ANNs with different structures were constructed and evaluated based on a dataset including 846 patients who had 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 including data preprocessing and neural network designing were done in MATLAB environment.
Results: The effectiveness of ANNs was evaluated in terms of classification performance using 10-fold cross-validation procedure and the results showed that the best model was a network that had 7 neurons in its hidden layer with an average absolute error of 0.1, turbulence 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. Although no system can be guaranteed to achieve 100% accuracy, they can be effective in reducing medical errors.

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