Clinicians' Knowledge and Perception of Telemedicine Technology

INTRODUCTION: Telemedicine is an application of information and communication technology in the healthcare environment. This study aimed to compare knowledge and perceptions of telemedicine technology among different groups of clinicians. METHODS: This survey study was conducted in 2013. The potential participants included 532 clinicians who worked in two hospitals and three clinics in a northern province of Iran. Data were collected using a five-point Likert-scale questionnaire. The content validity of the questionnaire was checked, and the reliability was calculated using Cronbach's alpha coefficient (α = 0.73). RESULTS: The results showed that most of the clinicians (96.1 percent) had little knowledge about telemedicine. They perceived the advantages of telemedicine at a moderate level and its disadvantages at a low level. The knowledge of dentists about this technology was less than that of other groups, and as a result they were less positive about the advantages of telemedicine compared to nurses, general physicians, and specialists. CONCLUSION: The limited knowledge of clinicians about telemedicine seems to have influenced their perceptions of the technology. Therefore, providing healthcare professionals with more information about new technologies in healthcare, such as telemedicine, can help to gain a more realistic picture of their perceptions

Development of a fuzzy decision support system to determine the severity of obstructive pulmonary in chemical injured victims

Background: Chronic Obstructive Pulmonary Disease (COPD) is the most common known complication of exposure to mustard gas. Thus, all clinical guidelines have provided some recommendation for diagnosis, clinical management and treatment of this disease. Decision support systems are used to increase the acceptance of clinical guidelines. The purpose of this research is to develop a CDSS to determine the severity of COPD in chemical injured victims. Objectives: Development of a decision support system to determine the severity of COPD. Patients and Methods: First, the variables influencing to determining the severity of the disease was classified through studying the clinical guidelines. Then, the fuzzy model was implemented. To testing the system, the data from 50 patients were used. Results: the overall accuracy in determining the severity of the injury is equal to 92, these indicators reflect the proper functioning of the system to assist the physician regarding the diagnosis of chronic obstructive pulmonary disease and determining its severity. Conclusions: The CDSS has efficient results and satisfactory performance. Although, the medical expert systems cannot be expected to provide 100 percent correct responses, however, they can be useful in the areas of patient management, diagnosis and treatment planning. © 2015 Taha Samad-Soltani, Mostafa Ghanei, Mostafa Langarizadeh

Conceptual framework for developing a diabetes information network

Objective: To provide a conceptual framework for managing diabetic patient care, and creating an information network for clinical research. Background: A wide range of information technology (IT) based interventions such as distance learning, diabetes registries, personal or electronic health record systems, clinical information systems, and clinical decision support systems have so far been used in supporting diabetic care. Previous studies demonstrated that IT could improve diabetes care at its different aspects. There is however no comprehensive conceptual framework that defines how different IT applications can support diverse aspects of this care. Therefore, a conceptual framework that combines different IT solutions into a wide information network for improving care processes and for research purposes is widely lacking. In this study we describe the theoretical underpin of a big project aiming at building a wide diabetic information network namely DIANET. Research design and methods: A literature review and a survey of national programs and existing regulations for diabetes management was conducted in order to define different aspects of diabetic care that should be supported by IT solutions. Both qualitative and quantitative research methods were used in this study. In addition to the results of a previous systematic literature review, two brainstorming and three expert panel sessions were conducted to identify requirements of a comprehensive information technology solution. Based on these inputs, the requirements for creating a diabetes information network were identified and used to create a questionnaire based on 9-point Likert scale. The questionnaire was finalized after removing some items based on calculated content validity ratio and content validity index coefficients. Cronbach's alpha reliability coefficient was also calculated (α Total= 0.98, P < 0.05, CI=0.95). The final questionnaire was containing 45 items. It was sent to 13 clinicians at two diabetes clinics of endocrine and metabolism research institute in order to assess the necessity level of the requirements for diabetes information network conceptual framework. The questionnaires were returned by 10 clinicians. Each requirement item was labeled as essential, semi-essential, or non-essential based on the mean of its scores. Results: All requirement items were identified as essential or semi-essential. Thus, all of them were used to build the conceptual framework. The requirements were allocated into 11 groups each one representing a module in the conceptual framework. Each module was described separately. Conclusion: We proposed a conceptual framework for supporting diabetes care and research. Integrating different and heterogeneous clinical information systems of healthcare facilities and creating a comprehensive diabetics data warehouse for research purposes, would be possible by using the DIANET framework. � 2016 Hossein Riazi, Mostafa Langarizadeh, Bagher Larijani, and Leila Shahmoradi

Managing diabetes mellitus using information technology: A systematic review

Objective: To review published evidences about using information technology interventions in diabetes care and determine their effects on managing diabetes. Design: Systematic review of information technology based interventions. Research design and methods: MEDLINE®/PubMed were electronically searched for articles published between 2004/07/01 and 2014/07/01. A comprehensive, electronic search strategy was used to identify eligible articles. Inclusion criteria were defined based on type of study and effect of information technology based intervention in relation to glucose control and other clinical outcomes in diabetic patients. Studies must have used a controlled design to evaluate an information technology based intervention. A total of 3613 articles were identified based on the searches conducted in MEDLINE from PubMed. After excluding duplicates (n = 6), we screened titles and abstracts of 3607 articles based on inclusion criteria. The remaining articles matched with inclusion criteria (n = 277) were reviewed in full text, and 210 articles were excluded based on exclusion criteria. Finally, 67 articles complied with our eligibility criteria and were included in this study. Results: In this study, the effect of various information technology based interventions on clinical outcomes in diabetic patients extracted and measured from selected articles is described and compared to each other. Conclusion: Information technology based interventions combined with the usual care are associated with improved glycemic control with different efficacy on various clinical outcomes in diabetic patients. © 2015 Riazi et al

Developing a fuzzy expert system to predict the risk of neonatal death

Introduction: This study aims at developing a fuzzy expert system to predict the possibility of neonatal death. Materials and Methods: A questionnaire was given to Iranian neonatologists and the more important factors were identified based on their answers. Then, a computing model was designed considering the fuzziness of variables having the highest neonatal mortality risk. The inference engine used was Mamdani's method and the output was the risk of neonatal death given as a percentage. To validate the designed system, neonates' medical records real data at a Tehran hospital were used. MATLAB software was applied to build the model, and user interface was developed by C# programming in Visual Studio platform as bilingual (English and Farsi user interface). Results: According to the results, the accuracy, sensitivity, and specificity of the model were 90, 83 and 97, respectively. Conclusion: The designed fuzzy expert system for neonatal death prediction showed good accuracy as well as proper specificity, and could be utilized in general hospitals as a clinical decision support tool. ©2016 Reza Safdari, Maliheh Kadivar, Mostafa Langarizadeh, Ahmadreaza Farzaneh Nejad, Farzaneh Kermani

Applying naive bayesian networks to disease prediction: A systematic review

Introduction: Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction. Objective: This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms. Methods: PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study. Result: In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms. Discussion: This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy. Conclusion: The method, termed NBNs is proposed and can efficiently construct a prediction model for disease. � 2016 Mostafa Langarizadeh and Fateme Moghbeli

Comparing Three Data Mining Methods to Predict Kidney Transplant Survival

Introduction: One of the most important complications of post-transplant is rejection. Analyzing survival is one of the areas of medical prognosis and data mining, as an effective approach, has the capacity of analyzing and estimating outcomes in advance through discovering appropriate models among data. The present study aims at comparing the effectiveness of C5.0 algorithms, neural network and C & RTree to predict kidney transplant survival before transplant. Method: To detect factors effective in predicting transplant survival, information needs analysis was performed via a researcher-made questionnaire. A checklist was prepared and data of 513 kidney disease patient files were extracted from Sina Urology Research Center. Following CRISP methodology for data mining, IBM SPSS Modeler 14.2, C5.0, C&RTree algorithms and neural network were used. Results: Body Mass Index (BMI), cause of renal dysfunction and duration of dialysis were evaluated in all three models as the most effective factors in transplant survival. C5.0 algorithm with the highest validity (96.77) was the first in estimating kidney transplant survival in patients followed by C&RTree (83.7) and neural network (79.5) models. Conclusion: Among the three models, C5.0 algorithm was the top model with high validity that confirms its strength in predicting survival. The most effective kidney transplant survival factors were detected in this study; therefore, duration of transplant survival (year) can be determined considering the regulations set for a new sample with specific characteristics. © 2016 Leila Shahmoradi, Mostafa Langarizadeh, Gholamreza Pourmand, Ziba Aghsaei fard, and Alireza Borhani

Differential diagnosis of Erythmato-Squamous Diseases using classification and regression tree

Introduction: Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. Objective: we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. Methods: we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. Results: The proposed model had an accuracy of 94.84 (Standard Deviation: 24.42) in order to correct prediction of the ESD disease. Conclusions: Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD. © 2016 Keivan Maghooli, Mostafa Langarizadeh, Leila Shahmoradi, Mahdi Habibi-koolaee, Mohamad Jebraeily, and Hamid Bouraghi

Quality improvement of liver ultrasound images using fuzzy techniques

Background: Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. Methods: In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. Results: With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. Conclusion: Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications. © 2016 Azadeh Bayani, Leila Shahmoradi, Mostafa Langarizadeh, Amir Reza Radmard, and Ahmadreza Farzaneh Nejad

An expert system to diagnose pneumonia using fuzzy logic

Introduction: Pneumonia is the most common and widespread killing disease of respiratory system which is difficult to diagnose due to identical clinical signs of respiratory system. Aim: In this research, to diagnose this, a structure of a fuzzy expert system has been offered. This is done in order to help general physicians and the patients make decision and also differentiate among chronic bronchitis, tuberculosis, asthma, embolism, lung cancer. Methods: This system has been created using fuzzy expert system and it has been created in 4 stages: Definition of knowledge system, design of knowledge system, implementation of system, system testing using prototype life cycle methodology. Results: The system has 97 percent sensitivity, 85 percent specificity, 93 percent accuracy to diagnose the disease. Conclusion: Framework of the knowledge of specialist physicians using fuzzy model and its rules can help diagnose the disease correctly. © 2019 Leila Akramian Arani, Frahnaz Sadoughi, Mustafa Langarizadeh