@article{ 
author = {Delaram, Zeinab and Safaei, Aliasghar and Atashi, Alirez},  
title = {Conceptual Modeling and Evaluation of Tele- Psychotherapy System based on Self-inventory for Patients with Depression}, 
abstract ={Introduction: Psychological anomalies have a significant prevalence in the world, especially in developing countries, such as Iran, and require the development of various interventions for diagnosis, treatment, monitoring and care services. Regarding the nature of psychological abnormalities monitoring, and especially the self-inventory testing method, it is recommended to use telemedicine capabilities for remote monitoring of patients with these abnormalities. The purpose of this study was to design and model a self-reported psychiatric system for depression. Methods: In this study, firstly, by reviewing the texts and observing similar systems, interviewing the psychiatric specialists of Shahid Beheshti Hospital of Kerman, nurses and patients of this center, and presenting and analyzing the questionnaire, the requirements of such a system were elicited and presented in the form of diagrams such as Class and business use Case in UML2.0. In order to provide a general view of prospective view, context diagram was presented. Scenario-based and simulation strategies were used to evaluate the model. Results: By reviewing 33 similar studies and 45 websites related to similar systems, seven subsets of account, education, counseling, treatment, monitoring, financial affairs and communications were defined and listed with the full definition of relevant items, and communication types were presented by UML diagrams. Also, in the first step of the evaluation of 20 scenarios, the final scenario was approved by experts. Simulation indicators represent the high efficiency of the system. Conclusion: The results of the evaluation of the proposed system show that the proposed system is satisfactory in terms of qualitative criteria and covers all expected functional requirements.  },  
Keywords = {Tele-monitoring, Self-inventory, Depression, Physical and logical modeling, Psychotherapy},
volume = {5},
Number = {1}, 
pages = {1-11}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-274-en.html},  
eprint = {http://jhbmi.ir/article-1-274-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Rasouli, Samira and Tabesh, Hamed and Etminani, kobr},  
title = {Evaluation of Artificial Intelligence Models and Classical Statistics Models of Time Series in Forecasting the Number of Hospital Inpatient Admissions}, 
abstract ={Introduction: The study and analysis of each health system has become a necessity for its performance improvement through time. In this context, management and analysis of the number of patients is an important factor in the process of improving managers' decisions. The aims of this study were to explore and evaluate the use of multiple time series forecasting methods to predict monthly hospital inpatient admissions at six public hospitals in Mashhad city and to compare the accuracy performance of these methods. Methods: This cross-sectional modeling study was performed based on monthly data of inpatient admissions at six public hospitals in Mashhad from March 2004 through March 2016. Data were extracted from database of the Statistics Office of Mashhad University of Medical Sciences. Holt-winters, Seasonal Autoregressive Integrated Moving Average (SARIMA), Multilayer Perceptron (MLP) and Generalized Regression Neural Networks (GRNN) models were applied to forecast monthly inpatient numbers at each hospital. The error of the models in regard to the predicted values was reported through Mean Absolute Percentage Error (MAPE). Results: Holt-Winters method, due to providing the optimal forecasting performance in four hospitals, could be an efficient method for predicting the number of inpatients in hospitals. Totally, the studied models with a MAPE from 2.13% to 4.12% showed acceptable performance in all six hospitals. Conclusion: Time series analysis is an adequate practical tool for predicting the number of hospital inpatient admissions. Given the unique characteristics of different hospitals, applied methods in this study, including modeling and data analysis can be used in other hospitals to improve their resource allocation and strategic planning.},  
Keywords = {Time Series Analysis, Inpatient Numbers, Holt-Winters, SARIMA, Neural Networks},
volume = {5},
Number = {1}, 
pages = {12-24}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-235-en.html},  
eprint = {http://jhbmi.ir/article-1-235-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Pajoohan, Mohamadreza and Gharaati, Zeinab},  
title = {Diagnosis of Leukemia Type by Machine Learning: Dimension Reduction and Balancing}, 
abstract ={Introduction: Combination of artificial intelligence and data mining has been resulted to considerable progress in the prevention and diagnosis of diseases. Complex models have been proposed for the diagnosis of acute leukemia from genetic information, but significant results have not been achieved. This study aimed to predict the type of blood cancer by examining a wide range of parametric and non-parametric methods and to increase the generalization of learning by extracting fewer essential features. Methods: This descriptive and analytical study used Leukemia1 dataset from the Vanderbilt University of USA. This dataset contains a set of bone marrow and blood samples of patients having leukemia used for classification based on three subgroups of leukemia, namely ALL B-cell, ALL T-cell and AML. Parametric classification including linear algorithms, Naïve Bayes, Euclidean distance, nearest average, template matching as well as non-parametric classification using basic estimator algorithms, kernel, k-nearest neighbors and k-nearest neighbors based on the kernel has been used. Results: Considering all features, the best method was nearest mean prediction method achieving the accuracy of 92.86%. By applying the PCA feature reduction method, too, the best result was related to the nearest mean algorithm and by average number of features of 6.8, the accuracy became 96%. Finally, using data-balancing methods and quadratic algorithm resulted in the average number of features and the accuracy of 5.41 and 98.59% respectively. Conclusion: The results show the effectiveness of essential features extraction in improving the accuracy of Bayes-based models and its preference over the existing complex models.  },  
Keywords = {Genetics data, Diagnosis of type of blood cancer, Data mining, Data balancing, Dimension reduction.},
volume = {5},
Number = {1}, 
pages = {25-34}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-251-en.html},  
eprint = {http://jhbmi.ir/article-1-251-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {ZamanpourBarkousaraei, Maryam and MohseniAzghandi, Mohamadreza and Godarzi, GholamRez},  
title = {Identifying and Prioritizing the Effective Factors in Commercialization of Mobile Health}, 
abstract ={Introduction: Mobile phone, as a manifestation of modern communication technology, has a strong position in human life and is an integral part of daily life. The health industry is also one of the main areas where mobile penetration in the industry has resulted in surprising advances and various challenges. Mobile health (activities and medical operations supported by the mobile phone) serves as a digital assistant for information, health and personal care. Health commercialization is a global phenomenon and many health systems in the world are affected by it. The aim of this study was to prioritize the factors influencing the commercialization of mobile health. Methods: In this applied study, through reviewing studies in the field of mobile health commercialization, 31 leading indicators of mobile health commercialization were identified and after obtaining the opinions of experts and explorative factor analysis, the identified indicators were categorized in 6 dimensions and ranked using a fuzzy hierarchy analysis. Results: The obtained indicators were respectively, service development, market management, organization and legal processes, technology and infrastructure, finances and large substrates. What is noteworthy in the proposed prioritization compared to the studied documents is the comprehensiveness of the relevant indicators and consideration of the various dimensions of commercialization in relation to similar researches. Conclusion: Regardless of the prioritization of effective factors on mobile health commercialization, a noteworthy point in this framework is significant importance of convergence of this technology with the economic structure governing health care system of the country, agreement of the technology with basic health infrastructures and methods of improvement of mobile health technology that undoubtedly, each of them, by itself, is an important factor in mobile health commercialization.   },  
Keywords = {Commercialization, Services, Superior technology, Mobile health},
volume = {5},
Number = {1}, 
pages = {35-43}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-270-en.html},  
eprint = {http://jhbmi.ir/article-1-270-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {zamanian, Hanieh and farsi, hass},  
title = {New Feature Extraction Method for Precision Improvement in Emotion Detection Using EEG Signals}, 
abstract ={Introduction: Since emotions play an important role in human life, it requires providing an intelligent method to detect emotions using electroencephalogram (EEG) signal based on signal processing techniques. In addition, in this research, using support vector machine (SVM) classifier with genetic evolutionary algorithm is a novelty in classification part. Methods: The proposed method focuses on feature extraction and classification of received signals from brain to improve emotion detection. In this way, firstly, effective EEG channels are identified and then time and frequency features of EEG signals are extracted and classified by an appropriate classifier. The proposed method is applied on DEAP database which includes recorded EEG signals by 32 people watching and listening 40 videos and music.     Results: The experiments show that selection of 7.5 seconds and 3 EEG channels provides acceptable results. In addition, the proposed method reduces computations and required memory and results in 93.86% accuracy for 4 emotion classification.   Conclusion: Improvement in emotion detection based on EEG signals contains several steps in which effective features extraction and classification are two important steps. According to this research, using time-frequency features of EEG signals and optimized SVM classifier with genetic algorithm provides better results.    },  
Keywords = {Emotion recognition, EEG signal, neural network and support vector machine (SVM)},
volume = {5},
Number = {1}, 
pages = {44-55}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-273-en.html},  
eprint = {http://jhbmi.ir/article-1-273-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Abdi, Marzieh and Saadatmand-Tarzjan, Mahdi and TaherzadehSani, Mohammad and Haghparast, Alirez},  
title = {A Review of Prediction Methods of Interaction Sites of Antibody-Protein Complexes Based on Artificial Intelligence}, 
abstract ={Introduction: Cancer is one of the most important health issues in the current and next centuries. Understanding the mechanism of interaction between antibody-protein residues is essential for designing targeted anticancer drugs based on monoclonal antibodies. Prediction of the effective structure is the first step for production of monoclonal antibodies. Methods: This paper is a systematic review of the state-of-the-art researches on prediction of interaction sites and specification of antibody structures. Artificial neural networks or web servers are frequently used for evaluation of interaction sites while some researchers have employed evolutionary algorithms for prediction of the effective structure of antibodies. Accordingly, 14 methods based on the protein spatial structure, 28 researches based on the molecular amino-acide sequence (without usage of the spatial structure), and 18 antigen/antibody structure prediction techniques were reviewed. Results: We demonstrated that the accuracy of structure-based methods can be increased up to 80% while the acuracy of sequence-based methods was rarely better than 75%. Since the spatial structure of many antibodies is unknown, some researchers raised the accuracy (even to 96%) by only antibody sequences able to interact with some similar antigens in training neural networks. Therefore, we suggest this approach for structure prediction of monoclonal antibodies because of its adequate high accuracy. Conclusion: In this paper, after reviewing available methods for prediction of antibody-protein interaction sites, some suggestions were made for effective prediction of structure of monoclonal antibodies.  },  
Keywords = {Immunology, Monoclonal Antibodies, Antibody-Protein Complexes, Artificial Intelligence, Neural Networks},
volume = {5},
Number = {1}, 
pages = {56-69}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-230-en.html},  
eprint = {http://jhbmi.ir/article-1-230-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {kamali, mahdieh and Sheikhtaheri, Abbas},  
title = {Documenting Nursing Reports Using Speech Recognition Technology: Benefits, Barriers, Challenges and Facilitators}, 
abstract ={Introduction: Documenting nursing reports and recording data are among the first and most important tasks of nurses. Speech recognition technology has been introduced as one of the most effective technologies in nursing documentation, which supports the nursing and patient interaction as well as the training of nurses through saving nurses' time. This study aimed to identify the benefits, barriers and facilitators of using this technology in documenting nursing reports. Methods: In this review study, various studies on the use of speech recognition technology in the field of nursing documentation published from 1990 to 2017 were reviewed at Pubmed, Science Direct, Google Scholar, Magiran, SID databases and, finally, ten papers were included in the review. The inclusion criteria were studies published in Persian or English language and the implementation of the speech recognition system in the field of nursing documentation and reporting. Results: Improvement of workflow, with the highest frequency, was the most important benefit. Reduction of the accuracy of speech recognition software was the first mentioned barrier of using this technology followed by the impact of environmental noises. Sufficient training and use of appropriate tools were found as the most important facilitators of using this technology. Conclusion: This study helps hospital managers, nursing managers and IT managers of hospitals to better manage selection and implementation of speech recognition system for documenting nursing reports. As a result, reducing productivity due to inaccuracy in voice recognition, lack of improvement in the quality of documentation of nursing reports, increasing the workload of nurses and their involvement in indirect care activities can be prevented.  },  
Keywords = {Speech recognition, Nursing documentation, Nursing reports, Nursing notes, Benefits, Barriers, Challenges, Facilitator},
volume = {5},
Number = {1}, 
pages = {70-82}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-268-en.html},  
eprint = {http://jhbmi.ir/article-1-268-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Kiani, Behz},  
title = {Geographical Information Systems in Health: Introduction and Application}, 
abstract ={The paper is a letter to editor, so it does not have an abstract.  },  
Keywords = {Geographical information systems, Spatial data, Spatial analysis, Health geography},
volume = {5},
Number = {1}, 
pages = {83-84}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-294-en.html},  
eprint = {http://jhbmi.ir/article-1-294-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Orooji, Azam and Langarizadeh, Mostaf},  
title = {Evaluation of the Effect of Feature Selection and Different kernel Functions on SVM Performance for Breast Cancer Diagnosis}, 
abstract ={Introduction: Breast cancer is one of the most common cancers affecting women. In mammography, differentiating a malignant tumor from a benign one is a very tedious task due to their structural similarities. Machine Learning (ML) is a subfield of Artificial Intelligence that can be used as an effective tool to help physicians to make decisions. Support vector machine (SVM) is one of the most common ML techniques that its performance depends on kernel parameters tuning and input features. The aim of this study was to investigate the effect of feature selection and different kernel functions on SVM performance. Methods: This analytic study was performed through comparative method. Genetic algorithm was used for feature selection. SVM models based on different kernel functions, including polynomial, Linear, Radial Basis Function (RBF), Quadratic and Multi-Layer Perceptron (MLP), were first performed with all features and then, with the selected features. The Wisconsin original breast cancer data set was used as a training set to evaluate the performance of the classifiers. All implementations were done in MATLAB environment. Results: According to the obtained results, by applying feature selection, the performance of SVM with MLP kernel function decreased and with quadratic kernel function increased. However, the performances of the linear and RBF kernels were desirable in both conditions. Generally, after the dimension reduction, the best accuracy, specificity, sensitivity and accuracy were dropped by 0.663, 0.833, 1.077 and 0.138 percent respectively. Conclusion: The ML-based methods can help physicians in diagnosis and decision makings for treatment.},  
Keywords = {Machine Learning, Dimension reduction, Clinical decision support systems, Early cancer diagnosis},
volume = {5},
Number = {2}, 
pages = {244-251}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-284-en.html},  
eprint = {http://jhbmi.ir/article-1-284-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Ayatollahi, Haleh and Gholamhosseini, Leila and Salehi, Masou},  
title = {Comparing the Performance of Data Mining Algorithms in Predicting Coronary Artery Diseases}, 
abstract ={Introduction: Cardiovascular diseases are the first leading cause of death worldwide. World health organization has estimated that the morality rate due to heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms will be useful in predicting coronary artery disease. The objective of the present study was to compare the accuracy of the CAD predictions made by ANN and SVM techniques. Methods: The present study was conducted via descriptive-analytical method. The research sample included all CAD patients hospitalized in three hospitals affiliated to AJA University of Medical Sciences from March 2016 to March 2017. Totally, 1324 records with 26 characteristics affecting the disease were extracted and after normalizing, and cleaning of the data, they were entered in SPSS statistics V23.0 & IBM Excel 2013; then, R3.3.2 data mining software was used to format data. Results: SVM model with lower MAPE (112.03) and higher Hosmer-lemeshow statistic (16.71), sensitivity (92.23) and specificity (74.42) yielded better fitness of data and provides more accurate prediction than ANN model. On the other hand, since the area under the ROC curve in SVM algorithm was more than that in ANN, it could be concluded that this model had higher accuracy. Conclusion: According to the results, SVM algorithm presented higher accuracy and better performance than ANN model and showed higher sensitivity and accuracy. It is suggested that in future studies, the results of the present study be compared with the findings resulted from applying other data mining algorithms.},  
Keywords = {Coronary Artery Disease (CAD), Data mining algorithms, Artificial Neural Network(ANN), Support Vector Machine (SVM)},
volume = {5},
Number = {2}, 
pages = {252-264}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-304-en.html},  
eprint = {http://jhbmi.ir/article-1-304-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Sheikhtaheri, Abbas and RostamiGaravand, Soghra and Ahmadi, Hossei},  
title = {Factors Related to the Acceptance of Radio Frequency Identification Technology in Health Information Management Departments of Hospitals Affiliated to Iran University of Medical Sciences}, 
abstract ={Introduction: Radio Frequency Identification (RFID) technology has been implemented in a wide variety of industries including healthcare field. Due to the introducing RFID technology in the healthcare industry, it is necessary to determine the factors influencing acceptance of this technology. Therefore, in the present study, the factors associated with the acceptance of RFID technology in Health Information Management hospitals affiliated to Iran University of Medical Sciences were studied. Methods: The current applied research was descriptive-analytic. A questionnaire was distributed among all health information management staff of the hospitals affiliated to Iran University of Medical Sciences (n=138) and finally, 135 questionnaires were completed. The results of the study were analyzed through SPSS16 and using descriptive statistics, inferential statistics, and regression. Results: The mean scores of factors were as follow: social effects (4.24 out of 5), management support (4.34 out of 5), expected performance (4.20 out of 5), expected effort (4.09 out of 5), facilitating conditions (3.47 out of 5), security (3.83 out of 5) and intention (4.3 out of 5). The coefficients of influence of social effects (0.125), management support (0.210), expected performance (0.244), expected effort (0.148) and facilitating conditions (0.135) showed positive significant relationships with intention to use RFID. However, security was not an influential factor on use of RFID. Conclusion: Based on the findings, providing general education, facilitating the use of this technology by vendors and developing adequate infrastructure may increase the willingness to accept RFID.},  
Keywords = {Technology acceptance in Health Care, Technology acceptance, RFID, Health information management department, UTAUT model},
volume = {5},
Number = {2}, 
pages = {265-273}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-265-en.html},  
eprint = {http://jhbmi.ir/article-1-265-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Hassanzadeh, Majid and Zabbah, Iman and Layeghi, Kamr},  
title = {Diagnosis of Coronary Heart Disease using Mixture of Experts Method}, 
abstract ={Introduction: Coronary Artery Disease (CAD) is one of the most common heart diseases and the main cause of mortality in men and women. This study aimed to predict the disease status using Neural Network compound (mixture of experts). Methods: The present study was a diagnostic study conducted on 200 patients referred to a heart specialty center in Torbat-e-Heydarieh. Patients' files contained their demographic information including13 risk factors. A model for predicting CAD based on multilayer perceptron neural network and mixture of experts was produced. Results: First, we used a neural network of multilayer perceptron with Propagation algorithm by different architectures. The best architecture could predict closed coronary artery with the accuracy of 71.7%. Then, by increasing the number of neural networks and training process, results were combined. Mixture of experts by liner method (majority voting) and nonlinear method (gating network) was applied and the accuracy rates of 75.8 percent and 78.3 percent were respectively obtained. Conclusion: Angiography is an invasive diagnostic procedure with risk factors such as stroke and heart attack. Therefore, non-invasive methods should be used for the diagnosis of CAD. In this study, with increasing the number of learners and their nonlinear mixture, the accuracy of diagnosis was increased.},  
Keywords = {Diagnosing Coronary Heart Disease, Artificial Neural Network, Mixture of expert},
volume = {5},
Number = {2}, 
pages = {274-285}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-216-en.html},  
eprint = {http://jhbmi.ir/article-1-216-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {NazariKamal, Mina and samouei, Raheleh and Sarafzade, Sheida and Ghaebi, Nafiseh and Moradi, Fatemeh and Moradzadeh, Mahtab},  
title = {The Effect of Education via Mobile Phones on Procrastination of Iranian Users: Designing a Treatment Aid Application}, 
abstract ={Introduction: Procrastination, in addition to wasting time, reducing personal performance and causing feeling of guilt, can cause failure of receiving treatment or counseling. Therefore, using accessible and easy-to-use technologies such as mobile phones can facilitate the treatment of people with this problem. This study aimed to investigate the effects of education via mobile phones on procrastination of Iranian users. Methods: The study was carried out using semi-experimental single group method on 32 Iranian users who had installed the counter procrastination application on their android mobile phones. The participants filled Basco’s procrastination questionnaire before and after the education. After two months of using the application, the results of pre-test and post-test were gathered and analyzed using student t-test. Results: Findings showed that education via mobile phones has been effective in reducing the procrastination in participants. Conclusion: This application, due to ease-of-use, high accessibility and saving cost, time and energy, can be useful in consulting, treatment and education centers.},  
Keywords = {Distance learning, Software, Mobile Phone, Procrastination, User},
volume = {5},
Number = {2}, 
pages = {286-292}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-262-en.html},  
eprint = {http://jhbmi.ir/article-1-262-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {SabbaghGol, Hame},  
title = {A Detection of Type2 Diabetes using C4.5 Decision Tree}, 
abstract ={Introduction: One of the most common diseases in the world is diabetes and the global prevalence of diabetes increases by about six percent annually. The use of data mining techniques to create predictive models is very helpful in identifying people at risk and reducing the complications of the disease. In this study, through using decision tree C4.5, methods of prevention and treatment of diabetes were investigated. Methods: In this applied and descriptive study, we used the standard UCI data and the pima-Indians-diabetes data set. This database contains 768 records with 8 fields. The analysis was done using Weka software using the CRISP3 methodology. In modeling decision tree, C4.5 was created using input variables and determining target variables. Also, the sensitivity, specificity, accuracy, as well as positive and negative predictive values were used to evaluate the model. Results: According to the model, high blood sugar levels, high gravidity, high age, high diastolic blood pressure, familial history and high BMI have respectively the highest effects on type 2 diabetes mellitus. The ranking rate was 73.8% and the accuracy of the C4.5 algorithm was 79%. Conclusion: Compared to the results of studies in the field of data mining for diabetes, the accuracy of the proposed algorithm is acceptable. The most effective factors on diabetes were identified. Also, rules were developed that can be used as a model to predict the risk of diabetes in people.  },  
Keywords = {Data mining, Type2 diabetes, C4.5 Decision tree},
volume = {5},
Number = {2}, 
pages = {293-303}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-281-en.html},  
eprint = {http://jhbmi.ir/article-1-281-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Hosseinpoor, Mohammadjavad and Parvin, Hamid and Nejatian, Samad and Rezaei, vahideh},  
title = {Detection and Extraction of Potential Promoter/Enhancer Interactions in Genome of Cancer Patients using an Evolutionary Multi-Objective Algorithm}, 
abstract ={Introduction: Cancer, as one of the most common diseases, has influenced the health of many people. The main aim of this study was to present a multi-objective evolutionary algorithm. The algorithm is capable of detecting and extracting potentially promoter/enhancer areas in the chromosomes of the affected people using the information concerning inter-genomic interactions. The correct extraction of these areas can help early diagnosis of cancer. Methods: In this applied and descriptive research, Hi-C data set including information on intergenomic interactions in the GM12878 cell was used. Multi-objective evolutionary algorithm was used in order to discover and extract potential promoter /enhancer interactions. The mentioned algorithm was implemented using MATLAB software. Furthermore, the efficiency of this algorithm was evaluated using two criteria. The first criterion is a proportional function that calculates the magnitude of inter-genomic interactions relative to the length of the genome regions; and the second criterion is the number of discovered potential promoters/enhancers. Results: The results and comparisons showed higher efficiency and optimality of the suggested method in discovering promoter/Enhancer interactions with variable length in comparison to HiCPro method. Therefore, the suggested method is able to discover the potential promoter/ enhancer interactions that cannot be discovered by HiC-Pro method. Conclusion: The suggested algorithm is able to optimally discover and extract potential promoter/ enhancer with variable length. This is a great help in medical science for early diagnosis of cancer.},  
Keywords = {Promoter, Enhancer, Hi-C Dataset, Multi-Objective Simulation Annealing Algorithm (MOSA), HiC- Pro Method},
volume = {5},
Number = {2}, 
pages = {304-313}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-245-en.html},  
eprint = {http://jhbmi.ir/article-1-245-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Feizmanesh, Farzaneh and Safaei, Ali Asghar},  
title = {Review of the Approaches and Techniques of Text Mining in Clinical Documentation}, 
abstract ={Introduction: With the extensive use of electronic medical records systems, a large amount of medical text data is produced daily in the hospitals and other medical environments that organizing this text information is important and necessary. Also, a need to automatically retrieve useful knowledge from this data to help clinicians is felt. In order to extract the hidden values in the medical text documents, text mining can be used in the field of health. Methods: In this review study, SID, Magiran, Pubmed, ScienceDirect, IEEE, and Google Scholar databases were searched with the keywords including "Text Mining” AND "Medicine” ،"Clinical Text Mining” AND "Predict”،, "knowledge discovery in medical text” and "Text Mining for Medical and Healthcare” in the English databases and keywords such as "text mining” AND " Discovering Knowledge in Medicine” in the Persian databases. Then, all articles that somehow refer to Medical knowledge discovery and text mining applications in the field of health were selected. Results: Text mining is one of the important and powerful techniques for extracting information from health information systems. Text mining in clinical data provides potential for new discoveries and it also improves efficiency and communication in hospital systems for doctors and hospital administrators. Conclusion: Nowadays, text mining in clinical documentation, is one of the developed technologies for the discovery of medical knowledge that its use in medical databases is essential to achieving immediate access to important health resources, and its application can improve patient care and reduce medical costs.},  
Keywords = {Text mining in clinical documentation, Text mining applications, Medical knowledge Discovery, Information extraction},
volume = {5},
Number = {2}, 
pages = {314-324}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-161-en.html},  
eprint = {http://jhbmi.ir/article-1-161-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {SadatBoveyr, Neda and RostamNiakanKalhori, Sharareh and Safdari, Rez},  
title = {A Proposed Model based on Information Technology for Parents’ Education to Improve Child Safety}, 
abstract ={Introduction: In children under the age of 5 years, unintentional injuries are the main cause of death. Parents play an important role in preventing these injuries. In all around the world, various technology-based interventions have been applied for improving parents’ knowledge and behavior regarding children safety. The aim of this study was to propose a technology-based model for promoting child safety. Method: This applied-descriptive study was conducted in two parts. In the first section, using a questionnaire, 20 physicians were asked to determine the most important information and educational elements. In the second part, the available technologies related to the prevention of unintentional injuries were investigated in terms of their impact on parents' knowledge and attitudes and finally, a technology was selected for the proposed model. Results: The findings in the first part included demographic data, clinical information, training needs including preventive behaviors, making environmental safety and first aid. In the second part, 10 technologies including kiosk, Internet/ web-based programs, computer software and mobile application were found. Mobile applications were the newest applied technologies in this area that had positive effect in change of behavior and knowledge. Therefore, using this technology, a model based on information technology was proposed. Conclusion: Use of information technology, through mobile applications, can provide an opportunity for improving parental knowledge and behavior toward child safety.},  
Keywords = {Safety, Children, Technology, Model},
volume = {5},
Number = {3}, 
pages = {325-336}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-306-en.html},  
eprint = {http://jhbmi.ir/article-1-306-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Foroozandeh, Elham and Derakhshan-Barjoei, Pouya and Bahadorzadeh, Mojdeh},  
title = {Investigation the Effect of Emotional Control and Extroversion on Severity of Central Serous Retinopathy in Patients Using fuzzy logic algorithm}, 
abstract ={Background and Objectives: Central serous retinopathy is one of the eye diseases and one of the risk factors of it is psychological stress. The aim of this study was to compare the emotional control  and extroversion in men and women with retinopathy and to examine the relationship between these two variables. It was also attempted to calculate the relative effect of emotional control and extraversion on the state and severity of retinopathy based on fuzzy logic algorithm. Materials and Methods: Roger and Najarian (1989) emotional control questionnaire and the 5 big factor personality test were used. After conventional selecting a sample, with a volume of 50 (40 male) from Isfahan ophthalmology hospitals, they were asked to answer the questionnaires. Results: There was no significant difference between the women and men in emotional control and extroversion. Extroversion showed a negative correlation with the subscale of emotional suppression (r = -0.36, p <0.009), and positive correlation with benign control (r = 0.38, p <0.006). Considering to the allocation of input and output parameters, their effects on retinopathy using fuzzy logic algorithm has been figured out.  According to the proposed fuzzy rules and ianterference engine the effect of variables categorized very well. Conclusion: Reviewing two important personality variables in retinopathy accompany with appropriate fuzzy algorithm were proposed to predict the effect of extroversion and emotional suppression on severity of retinopathy. This rate is achieved by applying the proposed fuzzy logic rules and algorithm. Statistical results showed the effect of the parameters well.  },  
Keywords = {Fuzzy logic, Emotional Suppression, Extroversion, Central serous retinopathy},
volume = {5},
Number = {3}, 
pages = {337-347}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-299-en.html},  
eprint = {http://jhbmi.ir/article-1-299-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Abbasi, payman and Kiavarz, Majid and Abbasi, Mohamm},  
title = {Spatial Analysis and Identification of High Risk Areas of Human Brucellosis by Spatial Autocorrelation Measurement Methods}, 
abstract ={Introduction: Brucellosis is one of the most common zoonotic infections, which is considered as a potential public health threat. Geographical information system (GIS) can be a great help to managers and policy makers in the public health field for designing, implementing and monitoring control programs. Method: In this descriptive cross-sectional study, the global Moran's I was used to determine the type of annual pattern of the disease, local Moran and Getis-Ord G * indices were used to identify the high risk areas and differential Moran's was used to compare the spatial pattern of the disease based on the incidence of annual incidence in the two successive years. Results: According to the results, during a 7-year period, most of the cases had been located in Malayer County and the least in Asadabad and Tuyserkan counties (central and western areas of the province). The incidence rate of the disease rose from April 2009 to March 2014 and decreased during April 2014 to March 2015. According to the results of global Moran's, the pattern of disease was cluster in all years except for 2012.  The clusters and hotspots detected by local Moran's I and G * were located in the southeast of the province. The results of differential Moran's I showed the emergence of new hazardous areas in the province over the study period. Conclusion: By comparing the spatial pattern of disease in different years, it can be said that the current control programs do not have the required efficacy and need a general overview. The results of this study can be used to identify starting points for future plans and to evaluate their effectiveness.  },  
Keywords = {Point pattern analysis, Human brucellosis, Hamedan, Global and local Moran’s I, Getis-Ord G*, Differential Moran’s I},
volume = {5},
Number = {3}, 
pages = {348-360}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-317-en.html},  
eprint = {http://jhbmi.ir/article-1-317-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Nooshyar, Mehdi and Momeni, Mohammad and Gharravi, Sorayya and Hourali, Fatemeh},  
title = {Using GBC Algorithm to Optimize Support Vector Machine Parameters for Predicting the Relationship between Cancer and Cardiac Infarction: A Case Study}, 
abstract ={Introduction: Awareness of cancer increases the probability of neurotic disorders and stress in the patient. Also, stress increases the risk of myocardial infarction. The present study aimed to determine the probability of a heart attack in cancer patients based on the GBC algorithm. Method: In this study, data were collected from the database of Shahid Sadoughi subspecialty hospital in Yazd. The medical records of 1679 patients with heart attack were studied, of which 81 ones belonged to patients with cancer. In the process of selecting features by the proposed model, if cancer is identified as an effective feature, then the relationship between cancer and cardiac infarction will be meaningful. Results: Using the proposed model, the cancer feature was selected to predict the probability of heart attack, which indicated a significant relationship between these two characteristics in patients who were vulnerable to cardiac disease. The predictive accuracy of the proposed model was 0.91 Conclusion: By choosing the cancer feature, the proposed model compared to other models has the least error rate and the most accuracy in predicting myocardial infarction. Naive bias method has maximum error rate and minimum accuracy. The simulation results indicate that in patients who are vulnerable to cardiac disease, after being diagnosed with cancer during the early months, heart attack is possible.  },  
Keywords = {Cancer, Heart attack, GBC algorithm, Support Vector Machine, Increase precision of prediction},
volume = {5},
Number = {3}, 
pages = {361-372}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-312-en.html},  
eprint = {http://jhbmi.ir/article-1-312-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {MahmoudiMeymand, Mohammad and Shayan, Ali and Hosseini, Seyedeh Masoumeh},  
title = {Effective Factors on the Adoption of Mobile Health Technology: A Case Study on Pregnant Women\'s Application}, 
abstract ={Introduction: Mobile Health refers to activities in the field of medicine and general health through using smart devices for gathering clinical healthcare data, presenting information about physicians, researchers and patients, supervising vital signs, and distant visits. Since using this accessible and inexpensive tool for healthcare education has not been developed yet in our country, this study was designed with the aim of identifying effective factors on the adoption of mobile health technology. Method: In this survey research, the study population was pregnant women referred to Hospitals in Karaj who completed the designed questionnaire. Data were analyzed using descriptive referential statistical methods (the single-variable t- test) and Friedman Test was used to prioritize the research variables. Results: According to the research findings, ffacilitating conditions had the most effect on the adoption of mobile health technology followed by the variables of social influence, performance expectancy, effort expectancy, waiting time, attitude, information quality, technology anxiety, data privacy and security, trust, and hedonic motivation. Conclusion: Facilitating conditions of using health mobile technology should be improved through increasing necessary financial, organizational and technical infrastructures of using health technology services and consequently increasing the acceptance of using this type of services. Also, with creating changes in thoughts, feelings, attitudes, the acceptance of this new technology should be facilitated.  },  
Keywords = {Technology adoption, Mobile health, Pregnant women, Survey, Application},
volume = {5},
Number = {3}, 
pages = {373-383}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-256-en.html},  
eprint = {http://jhbmi.ir/article-1-256-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {RezaiiFarokhzad, Maryam and SoleimanianGharehchopogh, Farh},  
title = {Determining Fuzzy Logic Parameters by using Genetic Algorithm for the Diagnosis of Liver Disease}, 
abstract ={Introduction: Liver disease is one of the most common chronic liver problems and cirrhosis.  Liver problems include a wide range of diseases and disorders that damage the liver tissue or its function. Early diagnosis and treatment of this disease can reduce the severity of the disease and mortality rate. Method: In this descriptive-analytic study, database was consisted of 583 independent records, including 11 features in the UCI machine learning database and through using fuzzy logic that its parameters are determined by Genetic Algorithm (GA), a method for the diagnosis of liver disease is proposed. For this purpose, first, the features of the dataset were ranked using the entropy feature and then, the dataset data were optimized using GA. Ultimately, liver disease was diagnosed using the genfis2 and genfis3 Fuzzy Inference System (FIS). Results: The results show that the accuracy of detection of liver disease using the genfis2 FIS with 8 features is 91.66% and using the genfis3 FIS with 6 features, it is equal to 89.87%. Moreover, the rates of error for genfis2 and genfis3 were 0.034 and 0.047 respectively. Conclusion: Liver disease is one of the most common diseases in population. Early diagnosis of disease while reducing costs can increase the chance of treatment success. According to the obtained results, the proposed model can identify people with liver disease with a fairly high degree of accuracy.    },  
Keywords = {Liver Disease Detection, Fuzzy Logic, Fuzzy Inference System, Genetic Algorithm},
volume = {5},
Number = {3}, 
pages = {384-397}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-261-en.html},  
eprint = {http://jhbmi.ir/article-1-261-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Abtahi, Hamidreza and Gholamzadeh, Marsa and Shahmoradi, Leil},  
title = {The Necessity of Development and Implementation of Twins Registry in Iran}, 
abstract ={Introduction: In the last three decades, national twins registries have been developed throughout the world with the aim of studying unique genetic characteristics and the possibility of determining the genotypic effects. This structured information can provide new vision in relation to the causes of diseases and psychological disorders. Method: In this analytic review, all active twins registries around the world were identified and their properties were examined by researchers. Additionally, PubMed, Scopus, Web of Science, Science Direct and Google Scholar databases were searched based on our search strategy. Finally, 69 articles related to the aim of study were selected. Results: Thirty founded national twins registries were analyzed in terms of the country, the goal and type of study, time of foundation, methods for information gathering, biobank samples, outcomes and unique characteristics. Conclusion: Considering the valuable results of twins registries in other countries, high potential of production of scientific documents and obtaining valid results and on the other hand, and the presence of more than 1,300,000 existing twins in Iran, developing a national Iranian twins registry and creating a biobank could be lead to creating one of the biggest national twins registries in the world. This, in turn, could be resulted in producing scientific publications in relation to the identification of cause of different diseases and mental disorders.},  
Keywords = {Twins, Registry, Medical researches, Health information management},
volume = {5},
Number = {3}, 
pages = {398-410}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-259-en.html},  
eprint = {http://jhbmi.ir/article-1-259-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {Hojati, Zohreh and Balkhi, Sayed Sharif},  
title = {Comparison of Wild Type and Mutated (mHuIFN-β 27-101) Interferon Binding to the IFNRA Receptor by Molecular Docking}, 
abstract ={Introduction: Interferon beta is one of the members of type I interferons. Creating R27T and V101F mutations is one of the important researches performed to improve function, decrease immunogenicity, increase expression and increase half-life of interferon beta. In this study, the effects of R27T and V101F mutations on interferon beta binding to interferon receptors were studied by molecular docking. Method: This study was performed through Bioinformatics methods. The required crystal structures were provided by the RCSB server. The simulations of R27T and V101F mutations were performed using online Rosetta Backrub software. Comparison of access to the solvent for the amino acids in the created structures was performed using asaview online server. Also, the effect of mutations on the structure and protein folding was investigated by the online Hope server and SPDBV software. The molecular docking between HuIFN-β and the external region of IFNAR receptor was performed using the online ClusPro2 protein-protein docking server. Results: The comparison of the values of the negative binding energy (ΔGbind) obtained from protein-protein molecular docking between IFNAR receptor and HuIFN-β, mHuIFN-β-27, mHuIFN-β-101 and mHuIFN-β-27-101 ligands did not show a significant difference (P> 0.99). Conclusion: Regarding these results, it can be concluded that the produced mutations do not have a negative effect on the forming of rHuIFN-β/IFNAR complex and does not interfere with the binding of the interferon beta to the receptor and thus improves the quality of the produced rHuIFN-β.  },  
Keywords = {Interferon Beta, Molecular Docking, IFNAR},
volume = {5},
Number = {3}, 
pages = {411-422}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-328-en.html},  
eprint = {http://jhbmi.ir/article-1-328-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2018}  
}

@article{ 
author = {RashidiKhazaee, Parviz and Bagherzadeh, Jamshid and Niazkhani, Zahra and Prinejad, Habibollah},  
title = {Proposing an Appropriate Architecture for Decision Support Systems in the Field of Complex Chronic Care:  Micro-Services Based Software Architecture in Kidney Transplant Care}, 
abstract ={Introduction: Development and successfully implementation of knowledge based clinical decision support system (KBCDSS) in kidney transplantation (KT) could support decision-making, reduce cost and improve quality of care. For practical use of these systems, however, many challenges have to be met.  Besides to well-recognized challenges of design and implementation of information systems in healthcare domain, designing an appropriate architecture for KBCDSS that can address requirements of complex and multidisciplinary chronic care, is a new growing challenge. Method: This case study was done based on the experience achieved in design of kidney transplantation management system in Urmia. Then, considering main applications and challenges of development and implementation of KBCDSS in Kidney transplantation and using the latest advancements in software technology field, appropriate software architecture based on micro-services has been proposed. Results: For each function (process) of the KBCDSS, three different micro-services of data gathering, optimal model designing and its application were proposed. These services, through implementation on provided backgrounds, can respond to different requests of patients and provide them with adequate knowledge and information. Conclusion: Use of this architecture makes it possible for different research teams with different skills and knowledge throughout the country to focus on designing a specific function and doing it in a best way. The proposed architecture can be extended and scaled. Moreover, in spite of increase in size and type of data, users and requests, it can respond well.  },  
Keywords = {Decision Support Systems, Clinical, Kidney Transplantation, Microservices, Software Architecture},
volume = {5},
Number = {4}, 
pages = {423-434}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-335-en.html},  
eprint = {http://jhbmi.ir/article-1-335-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2019}  
}

@article{ 
author = {Noferesti, Samira and ShemshadiNejad, Narges and Heydari, Fatemeh},  
title = {Using Data Mining Models for Differential Diagnosis of Iron Deficiency Anemia and β-thalassemia Minor}, 
abstract ={Introduction: One of the most common types of anemia is Iron deficiency anemia that its main differential diagnosis is β-thalassemia minor. The rapid and accurate screening of β-thalassemia minor has particular importance for pre-marriage medical counseling and the prevention of the birth of neonates with β-thalassemia major and differentiating it from iron deficiency anemia to avoid unnecessary prescription of iron. The aim of this study was to apply data mining techniques to differentiate iron deficiency anemia from β-thalassemia minor based on CBC test in order to increase the diagnostic speed and to reduce diagnostic costs. Method: The present study was a retrospective study and was performed on 1000 patients referred to Dr. Heidari laboratory of Zahedan city. To conduct research, CRISP-DM standard methodology and support vector machine data mining algorithms, naive Bayes, Bagging, Adaboots and decision tree have been used. WEKA software was used to analyze the data. Results: The results of the evaluations show that Bagging, Decision tree, Adaboots, support vector machine, and naive Bayes algorithms had respectively 95.73%, 95.5%, 94.6%, 80.2% and 76.6% accuracy in differentiating iron deficiency anemia from β-thalassemia minor. Conclusion: In this study, an automatic method based on data mining techniques for differentiation of iron deficiency anemia from β-thalassemia minor is presented. The results of the evaluations show that Bagging algorithm has higher accuracy compared to other data mining algorithms and differential indices. Also, with the help of the decision tree, rules have been extracted that can be used by the physician in timely diagnosis of the two diseases.  },  
Keywords = {Iron deficiency anemia, β-thalassemia minor, Differential diagnosis, Data mining, Bagging ensemble learning algorithm},
volume = {5},
Number = {4}, 
pages = {435-446}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-327-en.html},  
eprint = {http://jhbmi.ir/article-1-327-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2019}  
}

@article{ 
author = {Khosravi, Alireza and HosseiniSeno, Ami},  
title = {Detection of Abnormal Behaviors in Patients with Dementia and Preliminary Symptoms in Smart Home}, 
abstract ={Introduction: The number of elderly people who need help in their daily routines is increasing rapidly. Dementia is one of the most important causes of disability in elderly people and its outbreak has been a major burden on human societies. The purpose of this research was using intelligent home technology to monitor elderly behaviors, identify abnormal behaviors, and discover the initial signs of dementia before the onset of the disease. Early diagnosis of dementia at an early stage can lead to a high improvement in its treatment and delay the disease. Method: In this applied, descriptive-analytic study, the abnormal behavior and early symptoms of dementia were identified using machine learning techniques.  The kmedoide algorithm was used to analyze abnormal behaviors and to assess the quality of sleep as the primary symptoms of dementia, the valid PSQI questionnaire was used. Matlab 2012 was used for implementation. Results: The results in the abnormal behavioral section indicated that clustering algorithms have high efficacy in detecting abnormal behavior in smart home, and also results in early symptom examinations led to poor sleep recognition in the PSQI as a primary symptom of dementia. Conclusion: The behavior of the elderly, their abnormal behavior and early signs of diseases such as dementia can be recognized using the technology of the system under the supervision of the smart home.},  
Keywords = {Smart Home, Machine Learning, Clustering Algorithm, Abnormal Behavior, Dementia},
volume = {5},
Number = {4}, 
pages = {447-456}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-300-en.html},  
eprint = {http://jhbmi.ir/article-1-300-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2019}  
}

@article{ 
author = {Mousavi, Raahil and Sepehri, Mohammad Mehdi},  
title = {Comparison of Three Decision-Making Models in Differentiating Five Types of Heart Disease: A Case Study in Ghaem Sub-Specialty Hospital}, 
abstract ={Introduction: cardiovascular diseases are becoming the main cause of mortality and morbidity in most countries. This research goal was to predict the types of heart diseases for more accurate diagnosis by data mining and neural network technics. Method: This research was an applied-survey study and after data preprocessing, three approaches of neural network, decision making tree and Bayes simple algorithm were used to predict and recognize in Rapidminer software and neural artificial network model was used for prediction in Matlab software. Results: Genetic algorithm was used for selection of effective variables and neural artificial network models, decision making tree and Bayes simple algorithm were used to predict types of heart diseases in data mining. AHP model was used to determine a model with the best performance for predicting types of heart diseases. Conclusion: Neural network had much better performance than other data mining models used to diagnose types of heart diseases in this research. Also, in detecting disease by artificial neural network, the model with accuracy of more than 80 percent was verified as good and acceptable},  
Keywords = {Data mining, Neural network, Decision tree, Heart disease, Diagnosis},
volume = {5},
Number = {4}, 
pages = {457-468}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-314-en.html},  
eprint = {http://jhbmi.ir/article-1-314-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2019}  
}

@article{ 
author = {Javid, Bahareh and Fotouhi-Ghazvini, Faranak and Zakeri, Fahimeh Sadat},  
title = {Determining Blood Glucose Concentration using Near Infrared Spectroscopy: Early Findings}, 
abstract ={Introduction: Diabetes mellitus is one of the diseases that have grown dramatically in today's societies. People with diabetes should continuously measure their blood glucose level. Continuous blood glucose measurement by commonly used methods is painful and difficult. On the other hand, mobile phone can be a useful tool for accessing physicians and telemedicine services more easily. The aim of this study was to provide a medical care system for mobile monitoring of diabetes. Method: This study is an applied study and the proposed system includes a non-invasive sensor for measuring blood glucose using the near-infrared spectroscopy and an Android application. To evaluate the system, experiments were performed on glucose solution and on the body. In these experiments, both the transmittance and the reflectance modes were examined. Results: It was observed that with increasing glucose concentrations, the output voltage of sensor increased in the transmittance mode and decreased in reflectance mode. When testing on the body, the mean error rate was 34.14 in the transmittance mode and 35.9 in the reflectance mode. The results indicated that by combining transmittance and reflectance modes, better results were obtained. In this case, the mean percentage of error was 8.27 and RMSE was 52.18mg/dl. Conclusion: Finally, it can be inferred that near-infrared spectroscopy can be used in the future as an alternative for invasive methods in measuring blood glucose levels. There is also the possibility of providing remote medical services to diabetics using mobile phones.    },  
Keywords = {Telemedicine, Blood Glucose, Near-infrared Spectroscopy, Android Application, Diabetes},
volume = {5},
Number = {4}, 
pages = {469-480}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-276-en.html},  
eprint = {http://jhbmi.ir/article-1-276-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2019}  
}

@article{ 
author = {Ghanbari, Zahra and YousefiNejad, Mohsen and JafariNavimipour, Nima and Hosseinzadeh, Mehdi},  
title = {Detection of Twitter Users\' Attitudes about Flu Vaccine based on the Content and Sentiment Analysis of the Sent Tweets}, 
abstract ={Introduction: The influenza vaccine is one of the controversial challenges in today's societies. Considering the importance of using the flu vaccine in preventing the spread of influenza virus, the Twitter network, as a rich source of data, provides suitable conditions for research in this field to examine the attitudes of different people about this vaccine. The results in one hand will help health authorities to make more comprehensive decisions on long-term health plans for people with an awareness of the attitude of individuals towards the flu vaccine; on the other hand, it is concerned with data miners. Method: In this review study, approximately 1.220.539 tweets have been gathered from the Twitter social network during a one month period and have been clustered by using Mallet software. Categorizing users and separating tweets have done with an appropriate approximation. The content and sentiments of the selected tweets were analyzed and the locations of the users were checked. Results: In sentiments analysis, 76.28% of the tweets had a positive weight, 1.87% was neutral, and 19.68% had negative weight, indicating a positive attitude about the influenza vaccine. The location of users showed the highest rate of sending tweets from Canada, Britain and the United States, and Asian countries, and in particular, Iran, had a very small percentage of it. Conclusion: The highest rate of positive tweets were respectively sent by ‘individual’, ‘health’, and ‘organization’ groups indicating the global acceptance of the influenza vaccine and the success of the global health organizations on vaccination.  },  
Keywords = {Social Network, Twitter, Influenza, Flu, Influenza vaccine},
volume = {5},
Number = {4}, 
pages = {494-481}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-272-en.html},  
eprint = {http://jhbmi.ir/article-1-272-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2019}  
}

@article{ 
author = {Khorasani, Maryam and Shahbazi, Shirin and Mahdian, Rez},  
title = {Bioinformatics Study of the miR-200 Family and the Target Genes in Prostate Cancer}, 
abstract ={Introduction: Considering the limitations of the common diagnostic test for prostate cancer prostate cancer, the introduction of higher-specific biomarkers for a more accurate and timely diagnosis of prostate cancer is desired. In this study, we aimed to investigate the miR-200 family (miR-200a, miR-200b, miR-200c, miR-141, and miR-429) and their target genes using bioinformatics prediction tools in order to propose potential diagnostic biomarkers for prostate cancer. Method: In this theoretical study, based on bioinformatics study of micro RNA, the DIANA TOLLS-mirPath v.3 database was used to search the target genes of the miR-200 family in the signaling pathways known in prostate cancer pathogenesis. Then, we confirmed the suggested genes by the online predictive tools such as MiRWalk, Targetscan and RNAhybrid. Finally, the functional role of target genes was investigated on the DAIVID bioinformatics database. Results: According to the results of this study, the E2F3 gene is the target of all family members of miR-200, BCL2 is the common target of miR-200b / miR-200c / miR-429 and CCNE2 is the common target of miR-200a / miR-141 subsets. Also, miR-200c and miR-429 can modulate the pathogenesis of prostate cancer by targeting CDKN1B and KRAS genes, respectively. Conclusion: The results of this study showed that members of the miR-200 family targeting the genes involved in important biological processes and molecular pathways of prostate cancer pathogenesis are likely to be effective diagnostic biomarkers in future experimental studies.  },  
Keywords = {Prostate cancer, miR-200 family, Bioinformatics},
volume = {5},
Number = {4}, 
pages = {495-506}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-313-en.html},  
eprint = {http://jhbmi.ir/article-1-313-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2019}  
}

@article{ 
author = {Rafati, Hossein and MolaviTaleghani, Yasami},  
title = {Feasibility Study for the Establishment of Telemedicine: A Review Study and a Suggestion for Iran}, 
abstract ={Introduction: Telemedicine is considered as an integral part of the management of health care services and health information systems. This study aimed to review the studies on the goals and applications of telemedicine in different countries and to present a suggestion for the health system of Iran. Method: In this systemic study using keywords of tele-medicine, electronic health، tele health, health care, system, technology, program and tele medical, the related studies in databases of Iranian National Medical Digital Library were reviewed from April 2000 to April 2017. Results: After applying the entry criteria and evaluating the quality of the studies, 70 eligible papers were selected. The main identified obstacles to the establishment of electronic health in Iran were vagueness and complexity of the IT infrastructure, cultural and educational problems, frequent changes of managers, inability to attract skilled IT staff in the field of electronic health, lack of a specified mechanism for funding electronic health systems and failure to develop technical standards. Conclusion: Telemedicine in Iran can transfer from the pilot phase to a stable major phase provided that it be cost-effective. Therefore, it is suggested that sufficient attention be paid by custodians to issues such as allocating adequate funding, raising the awareness of authorities regarding the necessary infrastructure requirements, and creating motivation and cooperation feeling using briefing and training sessions.},  
Keywords = {Telemedicine, Tele health, Electronic health, Information technology},
volume = {5},
Number = {4}, 
pages = {507-519}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-263-en.html},  
eprint = {http://jhbmi.ir/article-1-263-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2019}  
}