@article{ 
author = {Atamanesh, Nayere and langarizadeh, Mostafa and Hakimshooshtrai, Mitr},  
title = {Developing an Application for Screening and Determining Severity of Autism Spectrum Disorder in Primary Health Care Centers}, 
abstract ={Introduction: Autism Spectrum Disorder is a set of neurodevelopmental disorders that affects not only the child but also the entire family. The disorder is diagnosed by observing the child's behaviors and activities. Timely screening and determining severity improve the child's abilities. The main purpose of this study was to develop an application for screening and determining severity of autism spectrum disorder in primary health care centers. Method: This research was a developmental-applied study. For determining data elements, a researcher-made questionnaire was designed and need assessment was conducted based on the opinions of five pediatric psychiatrists. Based on the results, a prototype of the smartphone application was designed which was provided to 30 health workers in primary health care centers to evaluate its usability. Data analysis was done using descriptive statistics via SPSS software (version 16). Results: The participating physicians recognized all data elements of the questionnaire essential. Five areas included communication, gross motor, fine motor, problem-solving and personal-social were put in the software, each of which with full subclasses and explanations. The results showed that health workers evaluated the usability of the application at a good level. Conclusion: Smartphone applications could be helpful for health workers in early screening and determining severity of autism in order to enhance children's cognitive and functional activities and reduce referrals to more specialized centers for screening and determining severity of autism.},  
Keywords = {Autism Spectrum Disorder, Smartphone, Application, Screening, Determining Severity},
volume = {7},
Number = {1}, 
pages = {1-9}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-445-en.html},  
eprint = {http://jhbmi.ir/article-1-445-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Montazeri, Mitra and Ahmadinejad, Mahdi and Montazeri, Mahdieh and Montazeri, Mohadeseh},  
title = {Design and Implementation of a Fuzzy Intelligent System for Predicting Mortality in Trauma Patients in the Intensive Care Unit}, 
abstract ={Introduction: The intensive care unit is one of the most costly parts of the national health sector. These costs are largely attributable to the length of stay in the intensive care unit. For this reason, there are significant benefits in predicting patients' length of stay and the percentage of deaths in intensive care units. Therefore, in this study, a fuzzy logic based intelligent system was designed to predict the percentage of deaths in trauma patients in the intensive care unit. Method: Data needed to design the system were collected from patient files from 2010 to 2012. Then, the system was run using data collected from each file and the system diagnosis was compared with the final diagnosis recorded in the patient file. The proposed neuro-fuzzy model was compared with five other intelligent models. This comparison was calculated and evaluated based on sensitivity, accuracy, specificity, and the area under the ROC curve. Results: The accuracy of these six models was approximately 83%, 81%, 80%, 75%, 82% and 81%, respectively. Conclusion: The neuro-fuzzy model was evaluated as the best model and had the highest accuracy. This model also had the highest area under the ROC curve. Therefore, it is recommended to use neuro-fuzzy model to diagnose and predict the percentage of deaths in trauma patients in the intensive care unit. This is important in health-related research particularly in allocating therapeutic resources to people at risk.},  
Keywords = {Trauma Patients, Intensive Care Unit, Classification, Prediction, Intelligent Models, ANFIS Naïve Bayes, Trees Random Forest 1NN, AdaBoost, SVM},
volume = {7},
Number = {1}, 
pages = {10-19}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-360-en.html},  
eprint = {http://jhbmi.ir/article-1-360-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Abbasi, Ommolbanin and Ramezanpour, Mohammadreza and Khorsand, Reihaneh},  
title = {Predicting Survival of Patients with Lung Cancer Using Improved Adaptive Neuro-Fuzzy Inference System}, 
abstract ={Introduction: Lung cancer is the main cause of mortality in both genders worldwide. This disease is caused by the uncontrollable growth and development of cells in both or one of the lungs. Although the early diagnosis of this cancer is not an easy task, the earlier it is diagnosed, the higher will be the chance of treating. The objective of this study was to develop an optimized prediction model of the survival of patients with lung cancer based on patients’ characteristics through data mining approach. Method: In this applied-descriptive study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm and the Particle Swarm Optimization (PSO) algorithm were applied to predict the survival rate of patients with lung cancer. The Surveillance, Epidemiology and End-Results (SEER) database of Louisville University, USA was also utilized. The evaluation of this proposed model was conducted based on certain criteria including accuracy, precision, error and root-mean-square error. Results: The obtained finding indicate the outperformance of ANFIS through PSO algorithm vs. its counterparts in this context with a 99.80 accuracy for one-year survival, 99.74% for two-years and 99.66% for five-years on SEER dataset. Conclusion: Applying ANFIS through PSO in predicting the survival of patients with lung cancer is a strong measure. Compared with other models, this newly proposed model was of the highest accuracy and precision and of the lowest error rate. Therefore, it is suggested to apply this model for predicting survival of patient.},  
Keywords = {Data Mining, Survival Prediction, Lung Cancer, Survival Rate},
volume = {7},
Number = {1}, 
pages = {20-29}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-449-en.html},  
eprint = {http://jhbmi.ir/article-1-449-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Arian, Roya and MehriDehnavi, Alireza and Ghasemi, Fahimeh},  
title = {Improving biological activity prediction of protein kinase inhibitors using artificial neural network and partial least square methods}, 
abstract ={Introduction: Protein kinase causes many diseases, including cancer; therefore, inhibiting them plays an important role in the treatment of many diseases. Traditional discovery inhibitors of this enzyme is a time-consuming and costly process. Finding a reliable computer-aided drug discovery tools which can detect the inhibitors will reduce the cost. In this study, it is attempted to separate kinase inhibitors into two groups, active and inactive, using artificial neural network  and finally predict biological activities of the predicted active compounds by partial least square . Method: In this study, after extracting the molecular descriptors in order to avoid overfitting problem, dimensional reduction was applied using Genetic algorithm. Moreover, artificial neural network was applied to distinguish active compounds from inactive ones and the biological activities of the small molecules were predicted using partial least square linear regression. Results: The results show that accuracy of the Neural networkmodel was improved from 74.45% to 86.7%, after reducing molecular descriptor dimensions. . The number of hidden nodes of this model was six with 86.7% accuracy, 83.4% sensitivity, 89.6% specificity and 73.2% Mathew's correlation coefficient. Moreover the partial least square linear regression model predicts the biological activity valuesby 85.8% correlation. Conclusion: The Neural network model and the partial least square linear regression model can sufficiently predict Kinase inhibitors and Genetic algorithm will improve the models performance},  
Keywords = {Protein kinase, Classification, Neural network, Regression, partial least square},
volume = {7},
Number = {1}, 
pages = {30-39}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-391-en.html},  
eprint = {http://jhbmi.ir/article-1-391-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Taghavifard, MohammadTaghi and Yousefvand, Akbar},  
title = {Assessing the Impact of Health Service Providers and Recipients on the Adoption of E-health Services}, 
abstract ={Introduction: By increasing the age of population, time considerations, the cost of health care services and the increase in unnecessary referrals to service centers, dissatisfaction among both health care providers and health service recipients are observed. By providing electronic services, part of this dissatisfaction will be eliminated. This requires the acceptance of e-health services. Given the cost of investment in this area for service providers, achieving the desired results is very important. Considering the limitations of the previous research works including  the lack of comprehensive studies, reliance on self-declaration and lack of paying attention to both the provider and recipient of services, investigation of factors which has the greatest impact on the acceptance of these services and removes some of the constraints of previous studies is of great significance. Method: This research was an applied-descriptive one. The statistical sample of this study included information technology experts and physicians. The measurement tool was a qualitative type based on interviews and questionnaires. The data were collected and analyzed using Delphi-Fuzzy method. Results: The values of defuzzified variables showed that the average values of all indexes were greater than 0.5, therefore, all indicators were confirmed. The research findings show that health care providers and recipients are both effective in using e-health services. Conclusion: Based on the findings of this study, it is suggested that managers and designers provide e-health services with taking into account the needs and preferences of both parties},  
Keywords = {Health Service Provider, Health Service Recipient, Acceptance of e-Health Services, Information Technology},
volume = {7},
Number = {1}, 
pages = {40-51}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-409-en.html},  
eprint = {http://jhbmi.ir/article-1-409-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Ghanizadeh, Akram and Rajabalipour, MohammadReza and ErshadSarabi, Roghayeh},  
title = {The Effect of Delivering Educational Programs through Telegram Messenger on Improving the Awareness of Learners
(A Case Study of Prevention of Hospital Infections among Health Workers in Bam)}, 
abstract ={Introduction: Hospital infections are one of the most important problems in each health care system the use of social media provides a powerful tool in the education process for health educators. The aim of this study was to evaluate the quality of the educational programs delivered through telegram messenger and its influence on improving the awareness of health workers in the prevention of hospital infections. Method: This was a descriptive-analytical cross-sectional study. All health workers in Bam health houses were enrolled in a census method. The trainings were provided to the target group through telegram messenger and the awareness of participants was measured before and after the training via questionnaires. Thereafter, the data were analyzed by SPSS-23 software. Results: In this study, the mean score of participants before and after training was 9.9 ±3.6 and 16.6 ±3.7 out of a total of 26 scores respectively; finally, the difference in scores before and after the training was significant. Conclusion: The use of social media and the telegram messenger has a positive effect on increasing the awareness of health workers about nosocomial infections.},  
Keywords = {Hospital Infections, Awareness, Virtual Learning, Telegram, Health workers},
volume = {7},
Number = {1}, 
pages = {52-59}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-492-en.html},  
eprint = {http://jhbmi.ir/article-1-492-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Einipour, Amin and Mosleh, Mohammad and Ansari-Asl, Karim},  
title = {A Graph-Based Clustering Approach to Identify Cell Populations in Single-Cell RNA Sequencing Data}, 
abstract ={Introduction: The emergence of single-cell RNA-sequencing (scRNA-seq) technology has provided new information about the structure of cells, and provided data with very high resolution of the expression of different genes for each cell at a single time. One of the main uses of scRNA-seq is data clustering based on expressed genes, which sometimes leads to the detection of rare cell populations. However, the results of the proposed methods mainly depend on the shape of the cell populations and the dimensions of the data. Therefore, it is very important to develop a method that can identify cell populations regardless of these obstacles. Method: In the proposed method, which was a library method, at first, the number of clusters (cell populations) was estimated. Estimating the number of clusters is important because in the real world, basic information such as the number and type of cell populations is not available. Thereafter, using a graph-based Gaussian kernel, while reducing the dimensions of the problem, the cell populations were identified by means of the kmeans++ clustering. Results: The results of the implementation showed that the proposed method can achieve an acceptable improvement compared to other machine learning methods presented in this regard. For example, for the ARI criterion, values of 100, 93.47 and 84.69 were obtained for Kolod, Buettner, and Usoskin single-cell data sets, respectively. Conclusion: The proposed method can cluster and thus identify cell populations with high accuracy and quality without having any basic information about the number and type of cell populations, regardless of the high dimensions of the problem.},  
Keywords = {Single-cell RNA-sequencing, Clustering, Identification of Cell Populations, Graph-based Gaussian Kernel},
volume = {7},
Number = {1}, 
pages = {60-72}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-420-en.html},  
eprint = {http://jhbmi.ir/article-1-420-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Toni, Esmaeel and Pirnejad, Habibollah and Niazkhani, Zahr},  
title = {The Content and Structure of Electronic Personal Health Records: A Systematic Review}, 
abstract ={Introduction: The electronic Personal Health Record (ePHR) improves people’s awareness and care management and leads to health promotion. One of the most important factors that contributes to the development of ePHR is identifying and understanding its content and structure. No comprehensive studies have so far been performed on the content and structure of ePHRs. Therefore, the purpose of this study was to systematically review the content and structure of ePHRs  in terms of system information resources, standards used, and their architecture. Method: This study was a systematic review. Studies published in English were searched using keywords and retrieved from PubMed, Scopus, Medline Ovid, ScienceDirect and IEEE databases from 2000 to 2019. Then, the articles were selected based on inclusion and exclusion criteria and evaluated on the basis of system information resources, design modules, the standards used and the architecture applied. Results: Finally, 38 original research articles met the inclusion criteria. The results of the present study showed that most ePHRs used tethered or hybrid architecture that allowed users to access ePHRs through web-based servers. Most ePHRs have also used electronic health records or medical electronic records as information resources through a data link. Standards of terminology, content, exchange, security, privacy, and confidentiality of ePHR systems have also been reported in a number of studies. Moreover, the content of most ePHRs was varied due to the diversity of the target population of citizens and patients (including chronic patients). Conclusion: The results of this study can be used by researchers and designers in designing ePHRs for various purposes.},  
Keywords = {Electronic Personal Health Record, Systematic Review, Content and Structure, ePHR},
volume = {7},
Number = {1}, 
pages = {73-90}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-479-en.html},  
eprint = {http://jhbmi.ir/article-1-479-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {GhayoumiZadeh, Hossein and Fayazi, Ali and Binazir, Bita and Yargholi, Mostaf},  
title = {Extraction of Suitable Features for Breast Cancer Detection Using Dynamic Analysis of Thermographic Images}, 
abstract ={Introduction: Thermography is a non-invasive imaging technique that can be used to diagnose breast cancer. In this study, a method was presented for the extraction of suitable features in dynamic thermographic images of breast. The extracted features can help classify thermographic images as cancerous or healthy. Method: In this descriptive-analytical study, the images were taken from the IC/UFF database. A total of 196 people, including 41 cancer patients and 155 healthy individuals were investigated. Each person had 10 thermographic images and in total, 1960 images were analyzed. The images were captured using the FLIR ThermaCam S45 camera. The proposed model was presented based on a series of breast thermographic images of an individual to extract 8 suitable features.  The extracted features included mean, standard deviation, entropy, kurtosis, homogeneity, energy, skewness, and variance. Results: The extracted features were evaluated by the classifiers including the decision tree, support vector machine, quadratic symmetric analysis, and K-nearest neighbor algorithm using the ten-fold cross validation. The accuracy and sensitivity were 99% and 99.33% for decision tree algorithm, 98.46% and 95.12% for support vector machine algorithm, 100% and 100%, and 99% and 97.56% for K-nearest neighbor algorithm. Conclusion: The results of this study showed that among the first-order statistical features, mean difference, skewness, entropy, and standard deviation are the most effective features which help to detect asymmetry. The features extracted by the proposed model can help classify the individuals into healthy or cancer-affected by thermal images.},  
Keywords = {Dynamic Model, Thermography, Breast Cancer, Feature Extraction},
volume = {7},
Number = {2}, 
pages = {91-101}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-381-en.html},  
eprint = {http://jhbmi.ir/article-1-381-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Ashayeri, Mahyar and Rezaeipanah, Ami},  
title = {An Ensemble Classification Model for the Diagnosis of Breast Cancer Using Stacked Generalization}, 
abstract ={Introduction: Breast cancer is one of the most common types of cancer whose incidence has increased dramatically in recent years. In order to diagnose this disease, many parameters must be taken into consideration and mistakes are possible due to human errors or environmental factors. For this reason, in recent decades, Artificial Intelligence has been used by medical practitioners to diagnose this disease. Method: In this applied-descriptive study, the diagnosis of breast cancer using stacked generalization was presented in the form of an ensemble model based on MLP neural network, ID3 decision tree, and support vector machine methods. To improve the performance of the ensemble classification model, a new approach called separator block was used. This block is responsible for identifying instances that cause errors in the classification model. Results: In order to evaluate the accuracy of the proposed method, the Wisconsin database for breast cancer was used. The experimental results showed the superiority of the proposed method over other similar methods. The accuracy of the classification model presented on the WBCD, WDBC, and WPBC datasets from the Wisconsin database was 99.54%, 99.58% and 99.84%, respectively. Conclusion: Data mining algorithms can provide new and more cost-effective systems in the field of health and treatment that can diagnose breast cancer with high accuracy. In this study, modeling based on the stacked generalization technique was of high accuracy in the diagnosis of breast cancer.},  
Keywords = {Stacked Generalization, Data Classification, Wisconsin Database, Data Mining, Breast Cancer},
volume = {7},
Number = {2}, 
pages = {102-112}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-387-en.html},  
eprint = {http://jhbmi.ir/article-1-387-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Norouzkhani, Narges and Sepehri, Mohammad Mehdi},  
title = {Designing and Evaluating an Education-based Follow-up System for Cardiac Patients}, 
abstract ={Introduction: Chronic diseases are among the most challenging health issues in the world. Although no definitive treatment has been found for such diseases, electronic health strategies can dramatically reduce their complications by enhancing patients' awareness and monitoring their treatment. The main objective of this study was to design an education-based follow-up system for cardiac patients based on mHealth. Method: This research was an applied-developmental one. To determine the data elements, a questionnaire was developed and needs assessment was conducted with the faculty members at University of Mazandaran and 2 health system specialists. The data were analyzed using descriptive statistics and after identifying the entities, the conceptual model of the system was designed and implemented based on the Unified Modeling Language (UML). The questionnaire was finally provided to 30 cardiac patients to assess its usability and user satisfaction. The data were analyzed using descriptive statistics with SPSS Software (version 16). Results: Some modifications were made to the system based on end users’ opinions and ultimately the system was developed with 7 parts including interaction with physician, remote visit, training, notification of medication intake, prescription, follow-up monitoring of patients, and communication with the emergency ward. The final evaluation of usability and user satisfaction showed that users rated the program with a mean score of 7.17 out of 9 at a good level. Conclusion: Given that users evaluated the system as good, it can provide effective interaction between physician and patient and monitor patient care along with ongoing training to improve the treatment process.},  
Keywords = {Follow-up, Patient Education, Heart Disease, mHealth application},
volume = {7},
Number = {2}, 
pages = {113-123}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-430-en.html},  
eprint = {http://jhbmi.ir/article-1-430-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Tavakol, Elham and Nowzari, Vali and Pirzad, Ali and Amirhosseini, Seyed Ehsan and Abdolahi, Ali},  
title = {Providing a Model for Cost Estimation of Hospital Information System Software Design Using Continuous Decision Tree Algorithm}, 
abstract ={Introduction: The cost estimation of a hospital information system software refers to estimating the cost and time required to develop the hospital information system software prior to the start of the project, which will continue until the end of production and development of the system. Estimating the cost of software to produce hospital information system is one of the major concerns of project management in health companies. Cost estimation models that estimate the cost of system construction in the early stages of project construction, with minimal information available from the project, are useful and needed. Selection of an appropriate cost estimation method enables efficient control of time and cost of system construction. Method: In this retrospective study, 23 open source software projects for hospital information system were selected and the cost of software design and 16 independent variables of each hospital information system software were extracted. The data were then transformed into a test and training set and using a continuous decision tree algorithm, a prediction model was proposed to estimate the cost of designing a hospital information system. The algorithm was then evaluated with four other continuous algorithms. Results: In this study, the continuous decision tree algorithm was implemented using the 10-fold method and two parameters including mean squared error and mean absolute percentage error were used for evaluation. In the proposed model, error of 74.31 units was obtained for the mean squared error and 17% for the mean absolute percentage error. Conclusion: It was shown in this study that the proposed model had an acceptable error rate indicating that it performed better than similar methods and can be used to estimate the cost of اhospital information systems},  
Keywords = {Hospital Information System, Continuous Decision Tree, Cost Estimation, Data Mining},
volume = {7},
Number = {2}, 
pages = {124-132}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-425-en.html},  
eprint = {http://jhbmi.ir/article-1-425-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Mahmoodi, Soghra and AfsharKazemi, Mohammad Ali and ToloieEshlaghy, Abbas and Shadnoosh, Nosratollah},  
title = {Modeling a Smart Hospital Information Architecture Based on Internet of Things and Recommender Agent}, 
abstract ={Introduction: Today, healthcare organizations worldwide are aware of the significance of technology and its impact on the quality of care. Hospitals are one of the most crucial systems in which the utilization of information is particularly important for several reasons. Using discrete-event simulation and developing a recommender agent, this study aimed to allocate IoT devices to patients in such a way as to minimize the number of medical errors and the length of treatment. Method: To carry out this research, first, the current condition of the medical care system was modeled using discrete-event simulation. Then, the scenario of introducing IoT into the model was simulated. Finally, using agent-based modeling, the recommender agent was developed to optimize the allocation of IoT devices to the patients. Results: Implementing recommender agent in the simulation model indicated that using IoT and recommender agent in medical processes leads to reducing medical errors and length of treatment. Conclusion: Utilizing the IoT in medical processes reduces errors, although the extent of its effectiveness varies at different stages of treating various diseases. Since some disease-specific IoT devices overlap in their functions, and given the limited number of these devices in hospitals, it is recommended that a recommender agent be used to ensure maximum effectiveness. Recommender agents make informed decisions as to how IoT devices can be efficiently allocated to patients at each stage of their treatment.},  
Keywords = {Smart Hospital, Internet of Things, Recommender Agent},
volume = {7},
Number = {2}, 
pages = {133-149}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-443-en.html},  
eprint = {http://jhbmi.ir/article-1-443-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Moradi, Morteza and Modarres, Mohammad and Sepehri, Mohammad Mehdi},  
title = {Comparison of Classifier Algorithms in the Identification of Polypharmacy and Factors Affecting it in the Elderly Patients}, 
abstract ={Introduction: Prescribing and consuming drugs more than necessary which is known as polypharmacy, is both waste of resources and harm to patients. Polypharmacy is especially important for elderly patients; therefore, the factors affecting it must be identified and analyzed properly. Method: In this retrospective study, first, several classifier algorithms, i.e., C4.5, SVM, KNN, MLP, and BN for polypharmacy identification were compared in terms of performance using WEKA software. In this process, 16 new features were extracted alongside the four existing features from data on 81,677 prescriptions of 19,428 outpatients aged 70 to 95 years whose prescriptions were dispensed in pharmacies contracted by the Iran Health Insurance Organization- Tehran province. The performance comparison was done using corrected t-test with resampling. In order to identify the effect of elderly patients’ characteristics on polypharmacy, two important parameters of the C4.5 were optimized by grid search using 50% of the dataset and then run on the rest of the dataset. The resulted rules were then presented in the form of a decision tree and verbal expressions. Results: Paired comparison of the classifiers indicated better performance of C4.5 and BN compared to the others. C4.5 had the ability to identify the factors that affect polypharmacy. In addition, parameter tuning improved the accuracy and AUC of applied algorithms. It also reduced the size of the resulted decision trees as well as the number of generated rules significantly. Conclusion: The data mining approach and C4.5 can identify and explain the characteristics of the elderly effective on the polypharmacy. The higher percentage of visits to general practitioners and contacts with a limited number of pharmacies are the most important characteristics.},  
Keywords = {Data Mining, Classification, Polypharmacy, Rational Prescription and Use of Drugs, Elderly},
volume = {7},
Number = {2}, 
pages = {150-160}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-429-en.html},  
eprint = {http://jhbmi.ir/article-1-429-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Eslami, Majid and Mokhtarian, Ali and Pirmoradian, Mostafa and Seifzadeh, Seyed AliReza and Rafiaei, Seyed Masou},  
title = {Designing and Creating a Virtual Reality Environment and a Wearable Glove with Control and Evaluation Capability to Rehabilitate Patients}, 
abstract ={Introduction: Using modern technologies like virtual reality in rehabilitation can promise a great movement in medical sciences as sometimes, these technologies shorten the path of reaching the goal. In the present study, a virtual reality environment with the ability of physical interaction was designed to test and measure the hand motion, and a sample of electronic equipment was presented alongside the virtual reality to help the patients requiring rehabilitation, so that they can use the special and unique feature of this technology for faster improvement and easier access to the exercises in every location, especially at home. Methods: The design and manufacturing processes were performed in two sections: software and hardware. In the software section, the connection to the hardware section and the available processors and sensors in this section was provided under Windows Operating System by designing the virtual reality environment and the required coding, by using the artificial intelligence available on the software, and by defining the movement conditions. Results: Software-wise and hardware-wise investigation and evaluation of the designed and manufactured equipment were performed according to the type of services provided to 5 patients based on the criteria proposed by Martilla and James involving the importance and performance indicators. The testing and evaluation performed based on these indicators showed the rate of user satisfaction with the provided services and equipment.     Conclusions: The results obtained from this study showed that a new treatment method can be provided for rehabilitation by measuring the level and amount of the patient’s hand movement and transferring these movements to the virtual environment proportional to the real conditions. Thus, alongside traditional rehabilitation methods, this new method can be effective in the improvement and quicker return of the people in need of rehabilitation to normal conditions.},  
Keywords = {Virtual Reality, Gyroscope, Rehabilitation, Wearable Gloves, Arduino},
volume = {7},
Number = {2}, 
pages = {161-170}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-408-en.html},  
eprint = {http://jhbmi.ir/article-1-408-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Khazaneha, Mahdieh and Osareh, Farideh and Shafiee, Kave},  
title = {Visualizing Multiple System Atrophy Studies Based on Collaboration Network and Centrality Indices in Web of Science Database}, 
abstract ={Introduction: Social network analysis is an analytical method based on graph theories that identifies relationships between individuals or factors to analyze the social structures resulted from those relationships. The objective of this study was to analyze co-authorship and co-word networks based on scientometric indicators and centrality measures in the studies on multiple atrophy system disease published in Web of Science database from 1988 to 2018. Methods: In this descriptive-analytical case study, the articles published in Web of Science database from 1988 to 2018 were collected using medical subject headings (MeSH) and various scientometric techniques including co-word analysis, co-authorship analysis, and network mapping. Results: In this study, 6767 articles on multiple system atrophy disease were retrieved. These articles were written by 39184 authors from 3884 organizations and 80 countries and were collected from 832 journals. In this study, based on co-occurrence, 8 clusters in the subject area of multiple system atrophy disease were identified the most important of which were multiple system atrophy disease, Lewy body, orthostatic, hypotension, progressive paralysis, positron tomography, and genes. Conclusion: Scientometric studies on this disease show a thematic map that can be effective in policy-making for studies in this field. Moreover, by examining these indicators, the issues in this field were identified and through this, the cases that are less taken into consideration can be detected and investigated as future research topics.},  
Keywords = {Scientometrics, Multiple System Atrophy, Collaboration Network},
volume = {7},
Number = {2}, 
pages = {171-180}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-442-en.html},  
eprint = {http://jhbmi.ir/article-1-442-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {HomaieFasih, Arezou and Atashi, Alireza and AhsaniTehrani, Hojjat},  
title = {Prediction of Breast Cancer Metastasis Using Fuzzy Models based on Data from Iranian Breast Cancer Patients}, 
abstract ={Introduction: The metastasis of breast cancer, the spread of cancer to different body parts, is considered as one of the most important factors responsible for the majority of deaths caused by breast cancer in women. Diagnosing the breast cancer metastasis at the earliest stages helps to choose the best treatment and improve the quality of life for patients. Method: In the present fundamental research, the dataset of Iranian patients available at Breast Cancer Research Center of Motamed Cancer Institute in Tehran was utilized. This study used Mamdani fuzzy inference system, Takagi-Sugeno fuzzy inference system and adaptive neuro-fuzzy inference system (ANFIS) to predict breast cancer metastasis at early stages. Results: The best prediction error was obtained using adaptive neuro-fuzzy inference system based on fuzzy c-means approach. The opinion of the experts at Breast Cancer Research Center of Motamed Cancer Institute and the prediction error of the assessed model indicated that this prediction system is well-formed. Conclusion: The optimal proposed prediction system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice},  
Keywords = {Breast Cancer, Metastasis, Mamdani Fuzzy Inference System, Takagi-Sugeno Fuzzy Inference System, Adaptive Neuro-Fuzzy Inference System},
volume = {7},
Number = {2}, 
pages = {181-189}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-446-en.html},  
eprint = {http://jhbmi.ir/article-1-446-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Ayani, Shirin and Moulaei, Khadijeh and Mahabadi, Mohammad Jav},  
title = {Investigation of the Status of IoT-Based Health Information Systems in a Three-Dimensional Conceptual Framework}, 
abstract ={Introduction: The ability to transfer data over the Internet of Things (IoT) to make right and timely decisions through accurate data collection has provided incredible interactive power and has resulted in an intelligent world with automated decision-making capability. The objective of this study was to investigate the status of IoT-based health information systems in a three-dimensional conceptual framework. .Method: In this descriptive-applied study, first, a comprehensive literature review was conducted to extract studies related to the architecture of IoT-based health information systems from the PubMed, Scopus, Web of Science, and IEEE databases. Then, to analyze and classify the extracted information and reach a consensus on explaining the status of IoT-based health information systems, three sessions of contemplation were held with the formation of a specialized committee. Results: Based on the results of this study, the status of health information systems was elaborated according to the most important functions and applications in three levels including community communication, diagnostic and treatment protocols, and IoT infrastructure. The status of information systems in this framework reflects the relationship between different parts of an organizational structure in the field of health from data collection at the lowest level to knowledge production at the highest level. Conclusion: The practical concept of the IoT as an underlying infrastructure has been overlooked in the design of most current health information systems. Therefore, the proposed model can be used as a guide for designers and specialists in information technology and medical informatics in designing these systems leading to an increase in the design quality of the systems.},  
Keywords = {Internet of Things (IoT), Healthcare, Health Information Systems, Conceptual Framework},
volume = {7},
Number = {2}, 
pages = {190-200}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-495-en.html},  
eprint = {http://jhbmi.ir/article-1-495-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Naseri, Atefeh and Hasheminejad, Seyed Mohammad Hossein and Sharghi, Mehr},  
title = {Improving the Inference of Gene Expression Regulatory Networks with Data Aggregation Approach}, 
abstract ={Introduction: The major issue for the future of bioinformatics is the design of tools to determine the functions and all products of single-cell genes. This requires the integration of different biological disciplines as well as sophisticated mathematical and statistical tools. This study revealed that data mining techniques can be used to develop models for diagnosing high-risk or low-risk lifestyles for colorectal cancer. Method: In this retrospective study, a dataset consisting of information relevant to 84 patients and 225 healthy individuals with 25 attributes was collected. This information was on patients diagnosed from 2006 to the first quarter of 2014. The most widely used techniques in the medical informatics literature including support vector machine, Naive Bayes, decision tree, and k-nearest neighbor were used to develop the models. Results: The developed models are able to distinguish people's lifestyles efficiently. A well-developed non-technical measure can properly determine the true value of individual predictions, whether true or false, at actual costs, and indicate a true measure of the cost savings in the health system by each model. Among the developed models, only two models were able to meet the criteria set for use in the real world. Conclusion: The developed models should not only be technically evaluated, but should also be examined in terms of metrics accepted for the medical field as well as feasibility for real problem solving.},  
Keywords = {Gene Expression Regulatory Network, Gene Expression Regulatory Network Inference, Propagation Algorithm, Data integration},
volume = {7},
Number = {2}, 
pages = {201-213}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-440-en.html},  
eprint = {http://jhbmi.ir/article-1-440-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Safdarian, Naser and Naji, Mohse},  
title = {Detection and Classification of Emotions Using Physiological Signals and Pattern Recognition Methods}, 
abstract ={Introduction: Emotions play an important role in health, communication, and interaction between humans. The ability to recognize the emotional status of people is an important indicator of health and natural relationships. In DEAP database, electroencephalogram (EEG) signals as well as environmental physiological signals related to 32 volunteers are registered. The participants in each video were rated in terms of level of arousal, capacity, liking/disliking, proficiency, and familiarity with the video they watched. Method: In this study, a practical empirical method was adopted to classify capacity, arousal, proficiency, and interest by ranking the features extracted from signals using algorithms on EEG signals and environmental physiological signals (such as electromyography (EMG), electrooculography (EOG), galvanic skin response (GSR), respiration rate, photoplethysmography (PPG), and skin temperature. After initializing the signals from the database and pre-processing them, various features in the time and frequency domain were extracted from all signals. In this study, SVM and KNN classifiers, K-means clustering algorithm, and neural networks, such as PNN and GRNN were used to identify and classify emotions. Results: It was indicated in this study that the results of the classification of emotions using various methods and classifiers were well-established with high accuracy. The best accuracy results were obtained by applying the proposed method using SVM classifier based on features extracted from environmental signals (85.5%) and EEG signals (82.4%). Conclusion: According to the results of the classification of emotions in this study, the proposed algorithm provides relatively better results compared with previous similar methods.},  
Keywords = {Classification of Emotions, EEG Signals, Physiological Signals, Feature Extraction, Signal Processing},
volume = {7},
Number = {2}, 
pages = {214-231}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-392-en.html},  
eprint = {http://jhbmi.ir/article-1-392-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {ErshadSarabi, Roghayeh and Kohan, Nooshin and Shafian, Sara and Afzali, Faezeh and Fatehi, Farhad and SarafiNejad, Afshi},  
title = {A Comparative Study of the Clinical Informatics Fellowship Curriculum in Selected Universities of the World}, 
abstract ={Introduction: The American Medical Association has introduced the newest medical subspecialty field known as "Clinical Informatics" since 2011. In order to present similar courses in Iran, it is necessary to design and evaluate a training program. The comparison of educational systems improves the content and quality of design of an educational program. Therefore, the objective of this study was to identify and compare the clinical informatics curriculum in several top universities in the world. Method: The present study was an applied one conducted in 2019. The tool used in this study was a researcher-made checklist that was designed based on reviewing the texts and opinions of experts in group discussion sessions. Its validity was confirmed by seven experts in medical informatics, clinical specialties, and medical education and its reliability was confirmed using the equivalence method. The data were collected by referring to the university websites to complete the study checklist. Results: In most of the studied universities, the clinical informatics fellowship course was implemented according to ACGME standards and the main content of this course included four main topics: principles and concepts, clinical decision-making and improving the care process, health information systems, and guiding and managing change. Conclusion: The similarities were mostly related to the duration of the course and the opportunity to conduct relevant researches in the field of clinical informatics. Most of the differences were also related to the opportunity to teach clinical informatics and learners' participation in holding a journal club during the course.},  
Keywords = {Comparative Study, Clinical Informatics, Medical Informatics, Medical Education},
volume = {7},
Number = {3}, 
pages = {232-241}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-541-en.html},  
eprint = {http://jhbmi.ir/article-1-541-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Langarizadeh, Mostafa and Heydari, Mohammad and HakimShooshtari, Mitr},  
title = {Design and Development of an Educational Mobile Application for Autism Spectrum Disorder}, 
abstract ={Introduction: Autism is one of the most common neurodevelopmental disorders that is diagnosed late due to parents' unfamiliarity with the disorder. Lack of knowledge of parents is one of the major problems of this disorder. The objective of this study was to develop an educational smartphone-based application for autism spectrum disorder specific for parents. Method: This study was an applied-developmental one conducted in three phases. First, a questionnaire designed for assessing information needs as well as determining data elements and application features was completed by specialists in the fields of pediatric psychology and pediatric neurology in hospitals affiliated to Iran University of Medical Sciences. The data were analyzed and the application was designed based on the results and finally, in order to evaluate the usability and user satisfaction, it was presented to 34 parents. Results: The majority of the data elements were regarded as necessary by specialists. The main features of the application included training the signs and symptoms of autism, self-care approaches, and diagnostic screening test for the ages of 6, 12, 24, and 36 months. Usability evaluation showed that parents evaluated the application as good with the mean score of 7.6 (out of 9). Conclusion: Educational mobile application for autism specific for parents, in terms of diagnosis, self-care, and rehabilitation, could be helpful for early diagnosis and treatment of autism and accessible for training parents. Training diagnosis, self-care, and interaction with the autistic child was approved as an accessible tool by parents. },  
Keywords = {Autism Spectrum Disorder, Parents, Education, Application, Mobile Phone},
volume = {7},
Number = {3}, 
pages = {242-251}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-444-en.html},  
eprint = {http://jhbmi.ir/article-1-444-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Parsapour, Fereshteh and Peymani, Javi},  
title = {Using Data Mining Techniques for Intelligent Diagnosis of Severity of Depressive Disorder}, 
abstract ={Introduction: Implementing a method that can help individuals diagnose or prevent mental disorders can be an important step in preventing and controlling these disorders especially in the early stages. The objective of this research was to apply data mining techniques for intelligent diagnosis of severity of depressive disorder. Method: The present applied research was carried out by going to a number of psychiatric clinics in Tehran and investigating patients' medical records. A total of 420 subjects who responded to the Minnesota Multiphasic Personality Inventory (MMPI, 71 questions) were selected through convenience sampling as the sample of the study (300 subjects were diagnosed with different degrees of depression and 120 subjects showed no symptoms of depression). The answer sheet of MMPI and the diagnosis of the psychiatrist were used as data for developing the model by k--Nearest Neighbor (k-NN), Decision Tree, and Support Vector Machine algorithms. About 70 percent of the data were applied for training and 30 percent of the data were used for validating the model. MATLAB software was used for data analysis.  Results: The results of the evaluations showed that Decision Tree algorithm with accuracy of 99.16% had higher accuracy compared to other algorithms. Furthermore, by implementing the developed models on each question of MMPI, the influence of each question on evaluation was determined. Conclusion: Classifying patients with data mining approach and based on the most important characteristics can be a useful and effective tool for analyzing and improving the decision-making process of physicians regarding the treatment of patients.},  
Keywords = {Depression, Intelligent Diagnosis, Data Mining},
volume = {7},
Number = {3}, 
pages = {252-262}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-418-en.html},  
eprint = {http://jhbmi.ir/article-1-418-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {TabatabaeiKashani, Elham and Foroozandeh, Elham and Bagheri, Ayoub},  
title = {Line Bisection Test Software to Evaluate the Visual-spatial Functions in Schizophrenic Patients}, 
abstract ={Introduction: One way to evaluate visual-spatial functions is to use line bisection test. With the advancement of information technology, this study attempted to design a line bisection test software and investigate its efficacy compared to the conventional paper-pencil form in schizophrenic patients. Method: The present study was a causal-comparative one and line bisection test software was designed with visual studio 2015 exactly the same as the paper-pencil form. In the first stage, 30 healthy individuals were selected by purposive method according to the inclusion criteria and each of them took 3 software tests and 3 paper-pencil tests and the mean deviations were calculated from the midpoint of the line. Then, the results of the two tests were compared. In the next stage, to test visual-spatial performance, 15 schizophrenic patients were randomly selected from hospitalized psychiatric patients and both software and paper-pencil forms of line bisection test were performed for each patient. Results: Using one-way analysis of variance, the mean deviation from the mean in healthy individuals and schizophrenic patients was evaluated and the results showed no significant difference between software and paper-pencil forms (P<0.05). Due to the high accuracy of software implementation, the errors due to randomized responses are detectable by the software. Conclusion: The results of this study indicated that line bisection test software is a good alternative to paper pencil test to evaluate the spatial-visual performance of schizophrenic patients and this software can be used to evaluate cognitive functions, especially the parietal lobe injuries of schizophrenic patients.},  
Keywords = {Line bisection test, Schizophrenia, Software design},
volume = {7},
Number = {3}, 
pages = {263-272}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-451-en.html},  
eprint = {http://jhbmi.ir/article-1-451-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Abbasi, Reza and Nabovati, Ehsan and Raeesi, Ahmad and Ostadmohammadi, Faezeh},  
title = {Investigating the Quality of Persian Mobile Applications Related to Patients with Chronic Diseases}, 
abstract ={Introduction: Today, the use of mobile applications to help self-care in patients with chronic diseases has increased. The objective of this study was to investigate the quality of Persian mobile applications related to patients with diabetes and hypertension. Method: This analytical study was conducted on all Persian mobile applications related to diabetes and hypertension in 2019. The mobile applications were searched in the Café Bazaar app store using the keywords such as “diabetes”, “blood sugar” and “blood pressure”. The APPLICATIONs Scoring System was used in this study. Two evaluators assessed the applications and in cases of disagreement a third evaluator was consulted. Results: In total, 179 Persian mobile applications were investigated. The mean and standard deviation of the quality of applications related to diabetes and hypertension based on the applied tools were 7.31±1.49 and 7.22±1.24 out of 16, respectively. More than 75% of these applications were comprehensive and less than 7% used authentic scientific resources. More than 80% of these applications were user-friendly and could work offline. Conclusion: The results of this study showed that most of the Persian mobile applications related to diabetes and hypertension were at a moderate level. Most of the mobile application developers took into account the interactive aspects of applications but paid less attention to providing a scientific content. Evaluating the quality of health-related mobile applications is recommended to app store managers before low-quality applications are available to public.},  
Keywords = {Mobile Health, Quality, Mobile Applications, Diabetes, Hypertension},
volume = {7},
Number = {3}, 
pages = {273-281}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-463-en.html},  
eprint = {http://jhbmi.ir/article-1-463-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Golshan, Atena and PournasirRoudbane, Maryam},  
title = {An Exploration of Factors Affecting the Acceptance of Smart Healthcare Devices Using Integrated Technology Acceptance Model}, 
abstract ={Introduction: Recently, the Internet of Things (IoT) allows patients and healthcare providers to transfer the treatment process to the patients and enables them to manage the disease and receive help from the healthcare team and mobile devices. This has been considered as a promising solution to improve the quality of healthcare. The objective of this study was to investigate the factors affecting the acceptance of smart healthcare devices using an integrated technology acceptance model. Method: In this descriptive-survey research, 169 out of 300 patients of Gil hospital in Rasht were selected based on Morgan table. The questionnaires by Papa et al. and Li et al. were applied to test the hypothesis. The structural equation technique and t-test were used to analyze the data. Results: According to the research findings, the effect of “permeability”, “comfort”, “perceived social risk”, and “performance risk” on “perceived usefulness and ease”, the effect of “perceived usefulness and ease” on “attitude towards the acceptance of smart healthcare devices”, and finally, the effect of “attitudes toward the acceptance of smart healthcare devices” on the “behavioral intention to use these devices” were confirmed. Conclusion: The results showed that the integrated technology model can play an effective role in identifying and examining the factors affecting the acceptance of smart healthcare devices and can be utilized as an important source of information for the use of these devices.},  
Keywords = {Internet of Things, Smart Healthcare Devices, Perceived Usefulness, Perceived Ease, Integrated Technology Acceptance Model},
volume = {7},
Number = {3}, 
pages = {282-292}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-447-en.html},  
eprint = {http://jhbmi.ir/article-1-447-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Eslami, Majid and Pirmoradian, Mostafa and Mokhtarian, Ali and Seifzadeh, Seyed AliReza and Rafiaei, Seyed Masou},  
title = {Investigating the Effect of Virtual Reality Environment and Intelligent Control Panel on the Rehabilitation of Upper Limb}, 
abstract ={Introduction: Occupational therapy and performing specific motor activities are among the healing processes for injured people that should be followed by patients in need after the doctor’s prescription. The objective of this study was to evaluate the effect of using virtual reality environments and interacting with hardware designed for the treatment and rehabilitation of patients with upper limb injuries. Method: In this study, the design and manufacturing process consisted of two parts: software and hardware. In the software section, in order to access the virtual reality environment under the Windows operating system, the virtual reality environment was designed and coded and then the connection to the hardware and microcontroller section of the Arduino board was provided so that commands could be exported from the controller to the virtual reality environment and depending on the mouse motion, a movement in the virtual environment would be made. Results: Given the nature of this research, the criteria proposed by Martilla and James were used to evaluate the software. Moreover, Numeric Pain Rating Scale (NRS) was used to measure the effect of this method on reducing pain and the performance of virtual reality environment in reducing pain was evaluated. Conclusion: The results showed that using virtual reality for rehabilitation, with accordance to the type of graphics provided and the hardware designed, in addition to reducing treatment time and pain level, encourages the patients to use the software and continue the activities in a place other than the clinic.},  
Keywords = {Virtual Reality, Rehabilitation, Occupational Therapy, Arduino, Computer Graphics},
volume = {7},
Number = {3}, 
pages = {293-303}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-457-en.html},  
eprint = {http://jhbmi.ir/article-1-457-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Abedini, Amin and Jabarpour, Efat and Keshtkar, Abbasali},  
title = {Predicting the Risk of Osteoporosis Using Decision Tree and Neural Network}, 
abstract ={Introduction: Osteoporosis is one of the major causes of disability and death in elderly people. The objective of this study was to determine the factors affecting the incidence of osteoporosis and provide a predictive model to accelerate diagnosis and reduce costs. Method: In this fundamental descriptive study, a new model was proposed to identify the factors affecting osteoporosis. Data related to 4083 women were investigated with Clementine12, the data mining tool, to discover knowledge. Using data mining algorithms, including decision tree and artificial neural network, some rules were extracted that can be used as a model to predict the condition of patients and finally, the accuracy of the proposed models were compared. Results: This study examined several models on a number of different characteristics and compared the results in terms of accuracy to find the best predictive model. The classification accuracy of the MLP neural network model was 92.14% which was higher than that of the other algorithms used in this study. According to the identification of factors affecting osteoporosis, the risk of developing this disease can be predicted for a new sample. Conclusion: Healthcare organizations are always gathering a lot of information while this data is not used properly. This study showed that the hidden patterns and relationships in this data can be discovered and used to improve the quality of diagnostic and treatment services.},  
Keywords = {Osteoporosis, Data Mining, Decision Tree, Artificial Neural Network, Clementine},
volume = {7},
Number = {3}, 
pages = {304-317}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-453-en.html},  
eprint = {http://jhbmi.ir/article-1-453-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Fooladi, Saber and Farsi, Hassan and Farsi, Farim},  
title = {Adaptive Filtering Strategy to Remove Noise from ECG Signals Using Wavelet Transform and Deep Learning}, 
abstract ={Introduction: Electrocardiogram (ECG) is a method to measure the electrical activity of the heart which is performed by placing electrodes on the surface of the body. Physicians use observation tools to detect and diagnose heart diseases, the same is performed on ECG signals by cardiologists. In particular, heart diseases are recognized by examining the graphic representation of heart signals which is known as ECG. The ECG signals are accompanied by noise due to external sources or other physiological processes in the human body. Method: In this applied research, an adaptive filter based on wavelet transform and deep neural network was proposed to reduce the noise. The proposed method was a combination of wavelet transform, adaptive learning, and nonlinear mapping of deep neural networks. Deep neural network was used with an adaptive filter to reduce more noise in the ECG signal. Results: Signal-to-Noise ratio (SNR) was used as a criterion to evaluate the quality of the proposed method to remove noise. In fact, the objective of this research was to increase this ratio which indicates higher efficiency of the method based on wavelet transform and deep learning. Conclusion: The results of the simulation showed that the proposed method improved the removal of noise from the ECG signal about 9.56% compared to existing methods. The reason is that the coefficients extracted from adaptive filter were optimized using deep neural network so that it provided a low-noise waveform.},  
Keywords = {ECG Signal, Wavelet Transform, Deep Learning},
volume = {7},
Number = {3}, 
pages = {318-325}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-468-en.html},  
eprint = {http://jhbmi.ir/article-1-468-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Tarahomi, Elham and Fahimi, Hossein and Taghizadeh, Mohamm},  
title = {Quantitative Comparison of Tree Pairs Resulted from Gene and Protein Phylogenetic Trees for Sulfite Reductase Flavoprotein Alpha-Component and 5S rRNA and Taxonomic Trees in Selected Bacterial Species}, 
abstract ={Introduction: FAD is the cofactor of FAD-FR protein family. Sulfite reductase flavoprotein alpha-component is one of the main enzymes of this family. Based on applications of this enzyme in biotechnology and industry, it was chosen as the subject of evolutionary studies in 19 specific species. Method: Gene and protein sequences of sulfite reductase flavoprotein alpha-component, 5S rRNA sequences, and taxonomic tree were extracted from 19 selected bacterial species. Then, phylogenetic trees of 5S rRNA and gene and protein sequences were compared with each other and with taxonomic tree. Phylogenetic trees were drawn by Mega7 software using neighbor-joining algorithm and taxonomic tree was extracted using NCBI taxonomy browser. Results: By comparing the corresponding tree pairs, the percentage of equivalent species and the mean equivalence score of species were calculated for each tree pair. The gene-protein tree was allocated the highest scores in both quantities. In comparing the taxonomic tree with three other trees, gene-taxonomy tree achieved the highest percentage in the mean equivalence score and protein-taxonomy tree obtained the highest percentage of equivalent species. Conclusion: Based on the results of the present research, the best replacement for each of the trees investigated in this study regarding evolutionary relations was identified. In other words, this study helps detect which evolutionary tree can be replaced for another evolutionary tree.},  
Keywords = {Phylogenetic Tree, Taxonomic Tree, Sulfite Reductase Flavoprotein Alpha-Component Enzyme, 5S rRNA Gene, Tree Pair},
volume = {7},
Number = {3}, 
pages = {326-336}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-461-en.html},  
eprint = {http://jhbmi.ir/article-1-461-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Pashandi, Zaiddodine and Kazerooni, Hanif and Hassanpoor, Hossei},  
title = {The Investigation and Analysis of the Strengths, Weaknesses, Opportunities, and Threats of Wearable Electronic Technologies: A Systematic Review}, 
abstract ={Introduction: Wearable electronic devices, which are based on Internet of Things (IoT) and big data computing, are able to continuously collect and process the physiological and environmental data and exchange them with other tools, users, and internet networks. Therefore, despite their potential benefits in health monitoring, they can pose serious risks, especially in breach of privacy. Hence, the main question in this study was to identify the most important strengths, weaknesses, opportunities, and threats related to wearable electronic technologies. Method: In this study, StArt 3.4 software was used for systematic review. Studies until November 30, 2019 were searched for keywords in “Scopus”, “IEEE”, “PubMed”, “Springer”, “Magiran”, “SID”, and “Sivilica” databases and Google search engine. Results: After deleting duplicate and unrelated documents, 80 documents were selected for final review and were analyzed using descriptive statistics. Accordingly, the main identified strength, weakness, opportunity, and threat were “improving lifestyle and human capabilities”, “low data reliability and user interface”, “applications in health and medicine”, and “information abuse and privacy breach” with 97.5%, 92.5%, 94%, and 99% frequency, respectively. Conclusion: The results of this study showed that improving human capabilities and application in medicine and health care are the main driving forces for the development of wearable electronic technologies. Therefore, in order to take advantage of the opportunities and overcome the potential threats of this technology, planning for the development and application of indigenous knowledge, as well as the development of the required standards and rules, must be put on the agenda immediately},  
Keywords = {Wearable Electronic Technologies, Strengths, Weaknesses, Opportunities, Threats, SWOT analysis},
volume = {7},
Number = {3}, 
pages = {337-350}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-464-en.html},  
eprint = {http://jhbmi.ir/article-1-464-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {Ahouz, Fatemeh and Golabpour, Ami},  
title = {Missing Values in PIMA Diabetes Dataset and Not Considering It in an Article Accepted in that Journal}, 
abstract ={The article is a letter to the editor, so it has no abstract.},  
Keywords = {Diabetes, Missing Values, PIMA dataset, data mining},
volume = {7},
Number = {3}, 
pages = {351-353}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-435-en.html},  
eprint = {http://jhbmi.ir/article-1-435-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2020}  
}

@article{ 
author = {GhaderiNiri, Amin and Farajzadeh, Nacer and Baybordi, Elahe},  
title = {A Case Study of the Impact of Parental Diseases on the Probability of Hypertension Using Data Mining Techniques}, 
abstract ={Introduction: Hypertension is one of the most common health problems. As it has a major impact on other serious diseases such as cardiovascular diseases and strokes, and due to not having any specific symptoms, it is known as a silent killer. Therefore, proper diagnosis, control, and treatment of hypertension is crucial in health care systems and will indeed prevent the development of the other diseases affected by. Studies have shown a strong connection between some diseases of parents and the probability of having hypertension in children. Method: In this study, using data collected from two health centers in Ardabil province and applying data mining techniques and Rapid Miner software, the impact of parental diseases on the probability of hypertension in children was investigated. Results: The results indicated that using paternal medical history on hypertension, premature coronary artery disease, hypercholesterolemia, and hyperthyroidism has a significant role in predicting hypertension in children. Moreover, the performance of the predictive model, developed using the collected samples and data mining techniques, was enhanced by 5%. Conclusion: Some diseases of parents, especially hypertension, increase the risk of hypertension in children. However, individual characteristics are the major determinants of this complication.},  
Keywords = {Hypertension, Artificial Intelligence, Machine Learning, Data mining, Prediction, Paternal Medical History},
volume = {7},
Number = {4}, 
pages = {354-367}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-459-en.html},  
eprint = {http://jhbmi.ir/article-1-459-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Shahalinejad, Sahand and Nooshyar, Mehdi},  
title = {Evaluation of Retinal Optic Disc Segmentation in Patients with Glaucoma and Comparison with Other Methods of Medical Image Processing}, 
abstract ={Introduction: Glaucoma is the most common cause of blindness in some countries. In the meantime, the field of retinal image processing has been proposed in order to provide automatic systems for disease diagnosis. Among the methods of medical image processing, image segmentation is a process of identification and change in the display of an image. The objective of this study was to use the segmentation method and compare it with previous algorithms so as to be able to diagnose retinal optic disc more accurately. Method: In the present analytical study, using the image segmentation method, each pixel was assigned a label in such a way that pixels with the same label had similar characteristics. The optic disc segmentation was performed. Using MATLAB software, retinal images of patients with glaucoma were entered into the program and an ideal output was obtained. Results: The quantitative analysis of the obtained results showed a high accuracy (85%) for the proposed method for the segmentation of the retinal optic disc; thus, the results can be used to efficiently diagnose a person with glaucoma. Conclusion: The purpose of segmenting an image is to make raw data more usable for subsequent statistical processing. It is expected that in the future, feature extraction be more accurate, and more details be available to machine vision systems to identify objects in the images.},  
Keywords = {Glaucoma, Retinal Images, Optic disc, Image Segmentation},
volume = {7},
Number = {4}, 
pages = {368-375}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-502-en.html},  
eprint = {http://jhbmi.ir/article-1-502-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Koraei, Maryam and Ahmadi, Shamseddi},  
title = {Comparing MicroRNA Target Gene Predictions Related to Alzheimer\'s Disease Using Online Bioinformatics Tools}, 
abstract ={Introduction: The prediction of microRNAs related to target genes using bioinformatics tools saves time and costs of the experimental analyses. In the present study, the prediction of microRNA target genes relevant to Alzheimer’s Diseases (AD) were compared with the experimentally reported data using different bioinformatics tools. Method: A total of 41 microRNAs associated with 21 essential genes involved in AD were selected based on experimental results reported in previously published literature. Then, the prediction of the target gene for each microRNA was done using three bioinformatics tools, including MirTarget, TargentScan, and Diana-microT. The results of the predictions for all three tools considering the reported target genes were compared with each other.  Results: The results showed that MirTarget, TargetScan, and Diana-microT correctly predicted 66%, 61%, and 27% of microRNAs’ attachment to the previously reported target genes involved in AD, respectively. However, none of the tools could predict the attachment of 22% of the microRNAs to the target genes reported in the literature. Conclusion: It can be concluded that MirTarget and TargetScan can better predict the target gene for microRNAs involved in AD compared with Diana-microT. Considering the algorithm used in MirTarget, this bioinformatics tool provides more functional and accurate results in predicting the target genes for microRNAs and it is recommended for predicting the target genes of microRNAs. It can also be concluded that the reported target genes for microRNAs involved in AD need further investigations in some cases.},  
Keywords = {MicroRNA, Bioinformatics, Target Gene Prediction, Alzheimer Disease},
volume = {7},
Number = {4}, 
pages = {376-389}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-498-en.html},  
eprint = {http://jhbmi.ir/article-1-498-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Mazhari, Shahrzad and Sabahi, Abdolreza and Ghiasi, Sahar and Nikbakht, Roy},  
title = {The Effect of Short Message Service on Medication Adherence in Patients with Schizophrenia}, 
abstract ={Introduction: Schizophrenia is a chronic and disabling mental disorder and medication non-adherence remains a significant challenge for the patients and psychiatrists. The present study aimed to assess the impact of short-message service (SMS) on medication adherence in schizophrenic patients as compared with the control group. Methods: A total of 63 patients with schizophrenia were assigned to the intervention group (N= 32) and the control group (N= 31). The patients in the intervention group received a daily SMS that read, ‘‘Please remember to take your medication” at 8:00am and 8:00pm. Medication adherence was assessed by the self-report Medication Adherence Rating Scale (MARS) at baseline and in the 3rd month, and the 6th month. Results: Patients receiving SMS had a significantly greater improvement in medication adherence compared with the control group in the 3rd month, but there was no significant difference in the 6th month. Conclusion: The results of the present study showed that text message reminder for medication intake is an effective and feasible strategy for enhancing medication adherence in patients with schizophrenia.},  
Keywords = {Short Message Service (SMS), Schizophrenia, Medication Adherence, E-Health, Cell Phone},
volume = {7},
Number = {4}, 
pages = {390-397}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-477-en.html},  
eprint = {http://jhbmi.ir/article-1-477-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Shafiekhani, Sajad and Fatemi, Azam Sadat and NazariGolpayegani, Gelayol and Banihashem, Seyed Yashar and Jafari, Amir Homayou},  
title = {Dynamical Analysis of Yeast Cell Cycle Using a Stochastic Markov Model}, 
abstract ={Introduction: The cell cycle network is responsible of control, growth and proliferation of cells. The relationship between the cell cycle network and cancer emergence, and the complex reciprocal interactions between genes/proteins calls for computational models to analyze this regulatory network. Ample experimental data confirm the existence of random behaviors in the interactions between genes and proteins in gene regulatory networks. Genetic factors, regulatory dynamics at the microscopic level, transcription rates of genes, and many other factors that depend on variable environmental conditions cause random behaviors in the cell cycle network. Method: The aim of this study was to present a stochastic Markov model to simulate interactions between proteins in a complex network of fission yeast cell cycle and to predict the dynamics of proteins. We used local sensitivity analysis to investigate the relationship between the weight of protein / gene interactions with the probabilities of phase transition in the cell cycle. Results: Using this model, the probability of transition between different phases of the cell cycle in the presence of different levels of noise was investigated and it was proved that the cell cycle path has the highest probability among all possible pathways for the cell. By performing sensitivity analysis, the correlation between the weight of interactions between proteins and the probability of transition between different phases of the cell cycle was calculated. Conclusion: Our local sensitivity analysis revealed that how perturbation on parameters affect the transition probabilities between subsequent cell cycle phases, so it suggests testable hypotheses in the experimental environments. Also, the model of this study proves the stability of the cell cycle in the presence of moderate levels of noise.},  
Keywords = {Dynamic, Cell cycle, Fission yeast, Markov},
volume = {7},
Number = {4}, 
pages = {398-412}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-484-en.html},  
eprint = {http://jhbmi.ir/article-1-484-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Mohammadi, Shahriar and Ghanbari, Nazani},  
title = {A Distributed Authentication Model for an E-Health Network Using Blockchain}, 
abstract ={Introduction: One of the most important and challenging areas under the influence of information technology is the field of health. This pervasive influence has led to the development of electronic health (e-health) networks with a variety of services of different qualities. The issue of security management, maintaining confidentiality and data integrity, and exchanging it in a secure environment between trusted parties is a challenge for e-health networks. Reviewing previous studies revealed that providing a comprehensive and efficient model for authentication and secure exchange of information with a distributed approach while avoiding the single point of failure, which can meet the various needs of the e-health network, was necessary to fill the gap. In this study, it was attempted to introduce a secure authentication model for the e-health network by overcoming the limitations of previous articles on authentication in the e-health network and using the benefits of authentication models in various fields. Method: In this study, a secure and distributed authentication model was designed for the health network with a combination of blockchain and public-key infrastructure and its usability was presented in the form of an applicable instance (electronic prescribing). Results: This model showed that combining public-key infrastructure with blockchain can provide a secure, two-way, scalable, and distributed authentication with avoiding a single point of failure for the e-Health network. Conclusion: It can be concluded that in order to meet the security requirements of the e-health network, it is necessary to use peer-to-peer authentication models which are not dependent on a central server and the proposed model indicated that combining public-key infrastructure with blockchain can bring benefits and fulfill security requirements.  },  
Keywords = {E-Health, Authentication, Blockchain, Public-Key Infrastructure, Security},
volume = {7},
Number = {4}, 
pages = {413-424}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-505-en.html},  
eprint = {http://jhbmi.ir/article-1-505-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Alizadeh, Ismaeil and Gorouhi, Mohammad Amin and AghaeiAfshar, Abbas and Mirr, Im},  
title = {Design and Development of a Mobile-Based Application for Identification, Prevention, and Control of Head Lice and Survey of User Satisfaction}, 
abstract ={Introduction: Using mobile phones is one of the ways to increase people's knowledge and awareness. The objective of this study was to design and develop a mobile-based application for “identification, prevention, and control of head lice” and also to assess the satisfaction of mobile users with this educational application. Method: This descriptive cross-sectional study was conducted in two stages including design and development of an educational application for head lice and assessment of the level of satisfaction among 140 users using electronic questionnaires. Finally, the data were analyzed by SPSS software (version 24) using descriptive statistics indices. Results: The descriptive findings of this study showed that 61.4% of users participating in the study were women, 45.8% of them were in the age group of 20 to 30 years, and 42.1% had high school diploma. Moreover, more than 95% of users stated that the scientific content in the application was easy to understand and the data were up-to-date. Overall, the results revealed that 81% of users in this study preferred to use this educational application over reading texts when exposed to head lice infestation and the effect of using the educational information of this mobile-based application was reported to be at high level (61%). Conclusion: The target population including families involved with head lice, school teachers, health center experts, and medical students are recommended to use this educational application.  },  
Keywords = {Head Lice, Identification, Prevention, Control, Application, Mobile},
volume = {7},
Number = {4}, 
pages = {425-434}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-493-en.html},  
eprint = {http://jhbmi.ir/article-1-493-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Shahhoseini, Mehdi and Molavi, Newsha and Tabatabaiefar, Mohammad Amin and Sehhati, Mohammadrez},  
title = {Implementation and Optimization of Annotation and Interpretation Step of Next-Generation Sequencing Data for Non-Syndromic Autosomal Recessive Hearing Loss}, 
abstract ={Introduction: The precision and time required for analysis of data in next-generation sequencing (NGS) depends on many factors including the tools utilized for alignment, variant calling, annotation and filtering of variants, personnel expertise in data analysis and interpretation, and computational capacity of the lab and its optimization is a challenging task.  Method: An application software was designed and implemented in C# for optimizing the third step of NGS data analysis. In this study, annotation, filtering, and interpretation of NGS data were specifically optimized for non-syndromic autosomal recessive hearing loss disease. Results: Whole-exome sequencing data of a patient with a pathogenic mutation confirmed by familial genetic analysis, which contained a total number of 671829 variants after primary analysis, were evaluated by the implemented software. After filtering the variants based on a predefined BED file, 508 variants remained. According to the patient’s pedigree, in the next step of analysis, homozygote variants were selected and only 187 variants remained. After applying the population frequency threshold of 0.6% on gnomeAD and ExAC databases, the number of variants reached 110 and 3, respectively. The identified pathogen was approved by the results of Sanger sequencing done for family co-segregation. This analysis took about 15 minutes on a moderate PC. Conclusion: The designed software is a fully graphical one that has the capability of comparing, viewing, filtering, and merging input files without any coding. Moreover, it can construct a local database from the analyzed files and apply region constraints and user-defined thresholds on various fields of the database.},  
Keywords = {Next-Generation Sequencing, Annotation, Variant Effect, Variant Filtering},
volume = {7},
Number = {4}, 
pages = {435-444}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-478-en.html},  
eprint = {http://jhbmi.ir/article-1-478-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Montazeri, Mahdieh and Naji, Hamidreza and Janati, Asm},  
title = {Assessing the Security Status of Picture Archiving and Communication Systems (PACS) of Kerman University of Medical Sciences to Record Data of COVID-19 Patients}, 
abstract ={Introduction: Today, the use of picture archiving and communication system (PACS) in health care centers is increasing. Meanwhile, the security of data of patients with COVID-19 in this system and control of access to such data is considerably significant. Therefore, this study aimed to evaluate the security of medical picture archiving and communication systems for recording data of COVID-19 patients in health care centers affiliated to Kerman University of Medical Sciences. Method: The present study was a cross-sectional one conducted in 2020. The research population included information technology staff in health care centers affiliated to Kerman University of Medical Sciences. Data were collected using IHS HIPAA Security Checklist for three types of PACS systems in 12 health care centers of Kerman University of Medical Sciences. Data were analyzed via SPSS software version 24 using descriptive statistics and linear regression to measure the relationship between administrative, physical, and technical dimensions of the system. Results: In this study, 50% of the participants were female and 58% of them had more than 5 years of work experience. The results of the present study showed that the most significant challenge in the security of PACS systems was in the technical dimension and the least significant was in the physical dimension. Moreover, there was a significant relationship between administrative and physical dimensions (P <0.05). Conclusion: The security status of PACS has weaknesses and it seems necessary to plan so as to develop the necessary laws regarding the security of access to data of patients with COVID-19 and its observance by system support companies, user training, access control, and risk management.},  
Keywords = {Data Security, Picture Archiving and Communication System (PACS), Assessment, IHS HIPAA Security Checklist, COVID-19},
volume = {7},
Number = {4}, 
pages = {445-452}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-514-en.html},  
eprint = {http://jhbmi.ir/article-1-514-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {AllahyariFard, Najaf and Davari, Afrooz and Karbalaei, Atiye and Zamani, Mohammad Rez},  
title = {Investigation of Network of Proteins Effective in Esophageal, Gastric and Colon Cancers and Determination of Key Common Proteins Using In Silico Method}, 
abstract ={Introduction: The prevalence of gastrointestinal cancers, especially gastric and colorectal cancers, is increasing. Gastric cancer is the most common gastrointestinal cancer in Iran and the age of esophageal and colon cancers onset has decreased in Iran. Therefore, it is important to study the factors and proteins involved in these cancers. Use of computational tools is an effective way to study protein networks and identify the important proteins involved in these diseases. Method: At first identified proteins from esophageal, gastric and colon cancers were extracted from relevant databases and articles; then, the Protein-Protein Interaction (PPI) networks were mapped and analyzed for cross-linking and determination of key proteins using servers and software such as STRING 11.0, Cytoscape 3.7.3, NDEx 2.4.3, and UniProt. The CentiScaPe 2.2 plugin was used by examining the centrality indices (Betweenness, Closeness, Degree) and the CytoHubba plugin was used by different centrality indices for determination of the key nodes and the central common proteins. Finally, after investigation of the expression profiles of proteins with high centrality indices using GTEx v8, Expression Atlas, and GEPIA2, the final common proteins were determined in all three cancers. Results: The results showed that PLK1, CCNB1, CCNA2, BUB1B, and CDK1 proteins are the central common proteins in all three tissues. These proteins are highly correlated (R => 0.8) with each other and have significant expression differences in normal and cancerous tissues. Conclusion: PLK1, CCNB1, CCNA2, BUB1B, and CDK1 proteins can be used as diagnostic and therapeutic markers for esophageal, gastric, and colon cancers. Further studies on these proteins will have a bright future for the treatment of various types of gastrointestinal cancer.},  
Keywords = {Esophageal Cancer, Gastric Cancer, Colon Cancer, Interactive Protein Network, Central Protein, Systems Biology},
volume = {7},
Number = {4}, 
pages = {453-465}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-472-en.html},  
eprint = {http://jhbmi.ir/article-1-472-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Freidooni, Mahsa and Pirnejad, Habibollah and Niazkhani, Zahr},  
title = {Clinical Decision Support System: An Effective Tool to Detect and Manage Drug-Laboratory Interactions}, 
abstract ={Lack of proper linkage between a patient’s medications and the results of laboratory tests can lead to common medication errors called drug-laboratory interactions (DLIs). DLIs are among the major types of preventable medication errors in the treatment process. Application of a clinical decision support system (CDSS) for physicians and other health care providers to decrease DLIs can effectively improve the treatment process and care quality helping to prevent potential adverse drug events. Design and implementation of these systems requires measurements to tackle a number of challenges. The present article briefly reviews these challenges and provides solutions to increase their effectiveness. With informed design and implementation of DLI-CDSSs, it is hoped that these interactions will be decreased and the quality of prescriptions will be improved with regard to patients’ laboratory test results.},  
Keywords = {Clinical Decision Support System (CDSS), Drug-Laboratory Interaction (DLI), Challenges, Advantages, Review Study},
volume = {7},
Number = {4}, 
pages = {466-473}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-481-en.html},  
eprint = {http://jhbmi.ir/article-1-481-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}

@article{ 
author = {Noori, Tayebeh and Mehraeen, Esmail},  
title = {The Necessity to Use of the National Hemoglobinopathy Registry system for Providing Healthcare Services to Patients}, 
abstract ={The article is a letter to the editor, so it has no abstract.  },  
Keywords = {},
volume = {7},
Number = {4}, 
pages = {474-476}, 
publisher = {دانشگاه علوم پزشکی کرمان},
url = {http://jhbmi.ir/article-1-499-en.html},  
eprint = {http://jhbmi.ir/article-1-499-en.pdf},  
journal = {Journal of Health and Biomedical Informatics},  
issn = {2423-3870}, 
eissn = {2423-3498}, 
year = {2021}  
}