@article{ author = {Kheyrolahi‌, Rasul and Hoorali, Fatemeh and Sedaaghi, Mohammad Hossein and Ebrahimnezhad, Hossei}, title = {Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping}, abstract ={Introduction: Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive to the effects of noise and specular spot reflection. In addition, the images used in these methods usually have limitations, such as the viewing angle. Method: In the proposed algorithm, a stable method is offered to remove the effects of specular spot reflection in the pupil, and necessary preprocessing is done to detect the exact location of the pupil. An optimized color mapping algorithm is proposed and the mapping is calculated with the help of the LM algorithm to accurately determine the pupil boundary. This method does not impose any restrictions on the eye image and shape, and the angle of the pupil in the image can be in any shape and direction. Results: The proposed method does not assume any specific model as the final pupil boundary (circle or oval) and is robust to noise and specular reflection factors as well. This method has been able to accurately detect the pupil boundary with the accuracy of 98.8% using UBIRIS dataset and 98% using the collected data by authors. Conclusion: The method presented in this paper can be used to increase the accuracy in determining the internal and external border of the iris to diagnose diseases related to the pupil and iris, as well as identity identification based on iris tissue.}, Keywords = {Disease Diagnosis, Segmentation, Iris, Pupil, Color Mapping}, volume = {10}, Number = {1}, pages = {1-17}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.10}, url = {http://jhbmi.ir/article-1-752-en.html}, eprint = {http://jhbmi.ir/article-1-752-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Mohammadi, Mahnaz and Aminoroayaei, Parisa and Nazari, Arezo}, title = {Bioinformatics Evaluation and Signaling Network Analysis of Total Target mRNA and Related SNP Function in People with Gastric Cancer}, abstract ={Introduction: Stomach cancer is the fourth most common cancer and the second leading cause of cancer death in the world. In addition to significant costs, it imposes many problems on families that is a proper justification for identifying related biomarkers. Method: The miRBASE website was used to find bioinformatics information. In addition, mirWALK and miRNASN databases were searched to find miRNAs information. To find cancer pathways, the DAVID and KEGG databases were adopted. Results: The studies conducted on the first pathway reported that microRNA and has-miR-10a-3p, through binding to rs1049216 in the CASP3 gene, by exerting an inhibitory effect on this gene, prevents the development of cancer. In the second pathway, microRNA and has-miR-296-3p, by binding to rs1564483 in the BCL2 gene, by exerting an inhibitory effect on this gene, causes cancer. In the third pathway, microRNA and has-miR-194-3p, through binding to rs7177 in the CCND1 gene, by exerting an inhibitory effect on this gene, prevents the development of cancer. Conclusion: In this research, cancer pathways involved in cancer were predicted. By finding three SNPs on specific regions of the three above-said genes, we found that these SNPs can be the cause of stomach cancer and can be introduced as biological biomarkers.}, Keywords = {Bioinformatics, micro RNA, Gastric Cancer, SNP, Targeted Medicine, Helicobacter pylori}, volume = {10}, Number = {1}, pages = {18-27}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.11}, url = {http://jhbmi.ir/article-1-738-en.html}, eprint = {http://jhbmi.ir/article-1-738-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Hashembeigi, Fatemeh and Moshiri, Fatemeh and Asosheh, Abbas}, title = {Introducing and Evaluating an Architectural Model for Smart Tourism Health Insurance based on IoT and Cloud}, abstract ={Introduction: Tourism as one of the programs to earn money leads to economic growth; however, due to the non-providing of medical and health services by insurance organizations, the Iranian tourism industry has faced a recession. Therefore, this study aimed to provide a smart tourism health insurance architecture in the context of the Internet of Things (IoT) and cloud to serve tourists. Method: This practical study was conducted to achieve a smart tourism health insurance architecture. First, its requirements were determined by reviewing the related literature and open interview with four experts from Iran Insurance Corporation. Then, by examining different types of tourism health insurance architectural models, a proposed service-oriented layered architecture was presented on the platform of IoT and cloud. Finally, the proposed architecture was evaluated by the ATAM method. Results: The proposed architecture has two components (i.e., the requester and the service provider) with user, network, cloud, and database layers that are connected in a service-oriented manner. Also, the evaluation of the proposed architecture by the ATAM method showed that it can respond to the health needs of tourists. Conclusion: This architecture is presented on the platform of IoT and cloud that provides an intelligent background, and by recording people's data in personal risk profiles, it facilitates providing special tariffs and reducing tourism costs. Therefore, tourists will be able to travel with more confidence in controlling their health status.}, Keywords = {Health Insurance, Smart Tourism, Service-oriented Architecture, Architectural Evaluation, IoT, Cloud}, volume = {10}, Number = {1}, pages = {28-40}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.12}, url = {http://jhbmi.ir/article-1-742-en.html}, eprint = {http://jhbmi.ir/article-1-742-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Mousavi, Seyed Mohammad and Hosseini, Soodeh}, title = {A Convolutional Neural Network Model for Detection of COVID-19 Disease and Pneumonia}, abstract ={Introduction: COVID-19 has had a devastating impact on public health around the world. Since early diagnosis and timely treatment have an impact on reducing mortality due to infection with COVID-19 and existing diagnostic methods such as RT-PCR test are prone to error, the alternative solution is to use artificial intelligence and image processing techniques. The overall goal is to introduce an intelligent model based on deep learning and convolutional neural network to identify cases of COVID-19 and pneumonia for the purpose of subsequent treatment measures with the help of lung medical images. Method: The proposed model includes two datasets of radiography and CT-scan. These datasets are pre -processed and the data enhancement process is applied to the images. In the next step, three architectures EfficientNetB4, InceptionV3, and InceptionResNetV2 are used using transfer learning method. Results: The best result obtained for CT-scan images belongs to the InceptionResNetV2 architecture with an accuracy of 99.366% and for radiology images related to the InceptionV3 architecture with an accuracy of 96.943%. In addition, the results indicate that CT-scan images have more features than radiographic images, and disease diagnosis is performed more accurately on this type of data. Conclusion: The proposed model based on a convolutional neural network has higher accuracy than other similar models. Also, this method by generating instant results can help in the initial evaluation of patients in medical centers, especially during the peak of epidemics, when medical centers face various challenges, such as lacking specialists and medical staffs.}, Keywords = {Image Processing, Artificial Intelligence, COVID-19, Convolutional Neural Network}, volume = {10}, Number = {1}, pages = {41-56}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.13}, url = {http://jhbmi.ir/article-1-740-en.html}, eprint = {http://jhbmi.ir/article-1-740-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Bakhsham, Milad and Hosseinpour, Mahdi and Ayeneh, Masoumeh and Karimi, Hossein and Parandavar, Paris}, title = {Identifying and Analyzing the Application of the Internet of Things in Supply Pharmaceutical Chain Agility in post COVID-19}, abstract ={Introduction: The internet of Things has become more popular as a new technology since the advent of wireless technology, and attracted attention from supply chain management promoters. This study aimed to identify and analyze IoT applications in the pharmaceutical supply chain agility in the post-COVID-19 era Method: We first review previous studies in applications of IoT in pharmaceutical supply chain agility and identified the findings through qualitative content analysis. Then, the identified factors were given to 20 management and information technology experts to confirm and validate. Results: The results showed that IoT applications on the pharmaceutical supply chain agility in 9 components and 50 indicators. First of all, the most important factor is faster communication with other stakeholders (suppliers, manufacturers, distributors, and customers) using the IoT in the supply chain. Secondly, the use of IoT for drug production; manufacturers invest in technologies that lead to lower operating costs in the long term, because network devices and sensors do not make mistakes, do not need rest and training, and do not take vacations. Therefore, they increase the agility of the production process and are a reliable and cost-effective alternative for productivity in production. Conclusion: The wide applications of the proposed model indicate the need to consider the use of the IoT in the pharmaceutical industry supply chain in order to improve overall supply chain performance and focus on supply chain agility.  }, Keywords = {Internet of Things, Supply Chain, After the COVID-19, Medicine Industry, Agility}, volume = {10}, Number = {1}, pages = {57-69}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.14}, url = {http://jhbmi.ir/article-1-743-en.html}, eprint = {http://jhbmi.ir/article-1-743-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Nabovati, Ehsan and Bijani, Soroush and Bijani, Behz}, title = {Evaluation of the Smartphone Applications in Antibiotic Prescription}, abstract ={Introduction: One of the important challenges for physicians is the choice of the right antibiotics for various diseases. In this regard, a mobile application could be helpful. The main purpose of this study was to compare the quality of mobile applications designed for an antibiotic prescription to help physicians in choosing the right antibiotic. Method: In this study, English non-commercial apps from the Apple App Store and the Google Play Store were searched using relevant keywords. The apps were selected and independently scored by an infectious disease specialist and a pharmacist using the Mobile Application Rating Scale (MARS). Kendall’s coefficient of concordance was used to assess inter-rater agreement. The Kolmogorov-Smirnov test was used to verify the normal distribution of the quantitative variables. Spearman's rank-order correlation was applied to determine the relationship between MARS scores and quantitative background variables and Mann-Whitney U tests for dichotomous variables. - Results: In the initial search until August 2022, 13 apps were eligible for evaluation. The MARS score obtained by applications without in-app advertisements (median: 3.9, IQR: 3.4-6.1) was significantly higher than applications containing advertisements (median: 2.9, IQR: 2.3-8.1) (P=0.029). In the objective domain of MARS, The highest mean domain score belonged to the engagement section (3.9±0.4) and the lowest mean domain score belonged to the functionality section (3.5±0.5). Conclusion: This study indicated that apps designed to help physicians prescribe antibiotics meet acceptable criteria. Considering objective scores of MARS, lower scores in the “engagement” section demonstrated that designers have paid less attention to this section in comparison to the “information quality” section.}, Keywords = {Antibiotic, mobile Applications, Smartphone, Clinical Decision Support, MARS}, volume = {10}, Number = {1}, pages = {70-81}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.15}, url = {http://jhbmi.ir/article-1-739-en.html}, eprint = {http://jhbmi.ir/article-1-739-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Hajieslam, Faezeh and Javanmard, Zohreh}, title = {Investigating the Role of Clinical Decision Support Systems in Reducing Medical Errors}, abstract ={Introduction: This study aimed to investigate the role of clinical decision support systems in reducing medical errors from the perspective of physicians and nurses in the teaching and therapeutic hospitals. Method: This descriptive cross-sectional study was conducted in 2021-2022 in two teaching and therapeutic hospitals in Ferdows City, Iran. Physicians and nurses have participated in the research. In this study, the census method was used, and the research community was considered the research sample. The questionnaire of Ariyai et al. was used as a data collection tool. After collecting the questionnaires, the data were analyzed using descriptive statistics methods. Results: 42 medical staff were included in the study. From a physician’s point of view, decision support systems can be helpful by reducing the risk of severe allergic reactions and drug interactions (60%), quick access to patient records (50%), and computerized order registries (30%). From nurses' point of view, eliminating problems related to doctors' handwriting (25%), avoiding  disremember of repeating tests or imaging (18.8%), quick access to updated information during work (12.5%), and reducing the risk of embolism (9.4%) are the advantages of clinical decision support systems. Conclusion: due to the importance of developing a decision support system in hospitals and measuring the readiness of medical staff to adopt it, it is suggested that the necessary trainings be provided.  }, Keywords = {Clinical Decision Support System (CDSS), Medical Error, Physicians, Nurses}, volume = {10}, Number = {1}, pages = {82-90}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.16}, url = {http://jhbmi.ir/article-1-745-en.html}, eprint = {http://jhbmi.ir/article-1-745-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Javanmard, Zohreh and Ameri, Fatemeh and Golmakani, Sara and Heidary, Maede and Nasrolahi, Naghmeh and Rajabi, Erf}, title = {Mental Health Mobile Applications Used During the COVID-19 Pandemic: A systematic review}, abstract ={Introduction: During the COVID-19 pandemic, the use of mobile applications increased among people coping with mental health problems. This study aimed to review mobile applications developed or used during the COVID-19 pandemic. Method: In order to retrieve related articles, the keywords "COVID-19", "Mental health", and "Mobile application" were searched in Web of Science, PubMed, and Scopus databases without a time limit until April 12, 2022. After selecting the articles according to the entry criteria, the features of the introduced applications were checked. Results: Finally, 25 articles were included in the study, of which 20 applications were developed during the pandemic and five practical programs were already designed. Four applications were designed for medical staff, and 16 were designed for the general public, for each of students, children aged 3-9, COVID-19 patients, cancer patients, and both medical staff and patients with stress disorders one app was designed. The reviewed applications presented educational tips for overcoming negative and destructive thoughts, and getting information about a healthy lifestyle. Also, eight applications provided contractions between users and psychologists.  Conclusion: The results of this study can be used as a comprehensive source to examine the features and capabilities of mental health applications during the Coronavirus pandemic. It is suggested that by identifying the strengths and weaknesses of such applications, measures should be taken to develop more comprehensive applications that meet users' needs.}, Keywords = {Mobile application, Mental health, COVID-19, Systematic Review}, volume = {10}, Number = {1}, pages = {91-110}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.17}, url = {http://jhbmi.ir/article-1-751-en.html}, eprint = {http://jhbmi.ir/article-1-751-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Roustaei, Mohammad and Giveki, Davar}, title = {Diagnosis of COVID-19 Disease in X-ray Images Based on Deep Learning Methods and Combining Classifiers}, abstract ={Introduction: COVID-19 is a new virus that causes infection in the upper respiratory tract and lungs, and the number of deaths due to the disease has increased daily on the scale of a global epidemic. Chest X-ray images have been useful for monitoring various lung diseases and have recently been used to monitor COVID-19 disease. Method: In this research, a multi-stage process was used to recognize COVID-19 from X-ray images. In the first stage, pre-processing was done to normalize the data. In the second step, which is the most important step of the proposed method, feature extraction was done. The feature extraction operation was based on deep learning networks. After feature extraction, machine learning algorithms were used to classify images. The algorithms used in this section are support vector machine, nearest neighbor, and decision tree algorithms. The results of these categories are combined in the fourth step based on the majority vote. Results: The parameters used in this research are among the classification parameters, including precision, accuracy, recall, and F-criterion, which were obtained as 96.5, 92.25, 94, and 93, respectively. Conclusion: The results of the experiments show the acceptable efficiency of the proposed method because, in addition to reducing the calculations by the separable layer, the combination of categories and their weighting has been used to obtain the final result.}, Keywords = {COVID-19, X-ray Images, Feature Extraction, Deep Learning Networks, Machine Learning, Classification}, volume = {10}, Number = {2}, pages = {111-124}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.18}, url = {http://jhbmi.ir/article-1-733-en.html}, eprint = {http://jhbmi.ir/article-1-733-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Abnoosian, Karlo and Farnoosh, Rahman and Behzadi, Mohammad Hass}, title = {A Pipeline-based Framework for Early Prediction of Diabetes}, abstract ={Introduction: Diabetes is a chronic disease worldwide, with an increasing annual death rate. Many health professionals seek innovative ways to detect and treat it early. Rapid advances in machine learning have improved disease diagnosis. However, because of the small amount of labeled data, the frequency of null and missing values, and the imbalance of databases, creating an optimal predictor for disease diagnosis has become a great challenge. This study aimed to present a pipeline-based classification framework for predicting diabetes on two datasets of Indian diabetic patients with two classes (patient and healthy groups) and Iraqi with three classes (patient, healthy, and prediabetes groups). Method: An important part of this framework is preprocessing. Different ML models based on the One-Vs-One approach for the three-class mode are implemented in the framework. Because of the imbalance of the data set, besides the accuracy evaluation criterion, the area under the receiver operating characteristic (ROC) curve is also used. To increase the level of these two criteria, the Hyper-parameters of each model are optimized using optimization methods to build a powerful model with less training and testing time through various feature selection methods. Results: The proposed framework was assessed for diabetes prediction on two datasets of Indian and Iraqi diabetic patients. It was revealed that using AdaBoost for the Indian dataset (ACC=89.98, AUC=94.11) and random forest for the Iraqi dataset (ACC=98.66, AUC= 98.62), good accuracy and performance were obtained. Conclusion: Regarding ACC parameters, precision, accuracy, recall, and F1-Score, the pipeline-based framework has an optimal performance in predicting diabetes, therefore, it can be used in clinical decision support systems.}, Keywords = {Diabetes Prediction, Machine Learning, Classification, Pipeline, Feature Selection, The Area Under the Receiver Operating Characteristic Curve (AUC)}, volume = {10}, Number = {2}, pages = {125-140}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.19}, url = {http://jhbmi.ir/article-1-750-en.html}, eprint = {http://jhbmi.ir/article-1-750-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Hashemi, Zohreh and Sabahi, Azam and Paydar, Somayeh and Salehi, Fatemeh}, title = {Evaluating the Usability of a Medical Care Monitoring Center System Using Heuristic Method}, abstract ={Introduction: Simultaneously with the COVID-19 epidemic, many efforts were made to manage and treat the disease. One of the main activities was the development of health information systems to help monitor this disease, but due to the speed of the disease’s spread, there was not enough time to evaluate the usability of these systems. Therefore, the present research was conducted to evaluate the usability of the Medical Care Monitoring Center (MCMC) system. Method: In this cross-sectional descriptive study (2022), the usability of the MCMS system was evaluated using a heuristic evaluation method. The system’s usability was investigated based on the Nielsen’s usability principles. All the problems identified by evaluators were collected in a single list and the mean severity of each problem was calculated. Results: There were 41 usability problems. The lowest mismatch with usability principles was related to “Help and documentation” (n=1) and the highest one was related to "user control and freedom” (n=8) and “Match with real world” (n=7). In addition, most of the identified problems had a relatively high degree of severity and were placed in the category of major problems. Conclusion: Using the usability evaluation, a significant number of usability problems of the system were identified which, if not resolved, can cause increased errors, dissatisfaction, and decreased quality of information; therefore, it is necessary to solve these problems by the designers and developers of this system.  }, Keywords = {Usability, Exploratory Evaluation, MCMC, User Interface}, volume = {10}, Number = {2}, pages = {141-151}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.20}, url = {http://jhbmi.ir/article-1-790-en.html}, eprint = {http://jhbmi.ir/article-1-790-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {SorayaieAzar, Amir and Rezaei, Samaneh and BagherzadehMohasefi, Jamshid and Niazkhani, Zahra and Pirnejad, Habibollah}, title = {Early Prediction of Alzheimer’s Disease Using Interpretable Machine Learning Algorithms}, abstract ={Introduction: Alzheimer’s disease is one of the most common neurodegenerative diseases in adults. The progressive nature of Alzheimer’s disease causes widespread damage to the brain, and early diagnosis can manage the disease and slow down its progression effectively. Method: In this study, a dataset related to the early prediction of Alzheimer’s was used. Spark framework was used for data management and three machine learning algorithms including Naïve Bayes, Decision Tree, and Artificial Neural Networks with the best hyperparameters were implemented and compared. To prevent overfitting and measure the efficiency of the models, a 5-fold cross-validation method was utilized. Furthermore, a method was used for interpreting machine learning black box models. Results: The decision tree and artificial neural network models obtained 98.61% accuracy and 98.60% F1-Score in the Spark framework including one or three computers. Important features in the decision-making process of the artificial neural network were identified using the interpretability technique. In addition, the computational time required for training the proposed models was calculated through different approaches, and the use of multiple computers was 35.95% faster than a single computer. Conclusion: With increasing the number of Alzheimer’s disease patients around the world, the need for a decision support system using machine learning algorithms, which can predict the disease early in a huge amount of data, is felt more. Therefore, the machine learning models proposed in this study for early prediction of Alzheimer’s disease as an interpretable auxiliary tool in the decision-making process can help clinicians.}, Keywords = {Early Prediction, Alzheimer’s Disease, Machine Learning, Interpretability, Big Data}, volume = {10}, Number = {2}, pages = {152-164}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.21}, url = {http://jhbmi.ir/article-1-753-en.html}, eprint = {http://jhbmi.ir/article-1-753-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Langarizadeh, Mostafa and Rostampour, Amir Homayoun and NoshirvanRahatAbad, Fereydon and Langarizadeh, Mohammad Hossein and Sarpourian, Fatemeh and FatemiAghda, Seyed Ali}, title = {Design and Evaluation of a Mobile Phone-based Recommender System for Patients with Cardiac Arrhythmia}, abstract ={Introduction: Cardiovascular disease is one of the main causes of death worldwide. One of the important factors is readmission due to the lack of training. Considering the widespread use of mobile phones, this study aimed to design a recommender system for patients with cardiac arrhythmia. Method: This is an applied-developmental study that was conducted in a quantitative (descriptive) method and in 2 stages. First, the recommended educational parameters and application requirements of the software were determined based on literature review. Then, the obtained contents were provided to 16 cardiologists working in Shahid Rajaei and Hazrat Rasool Akram Hospitals in Tehran. To evaluate the usability, the application was given to 50 patients with cardiac arrhythmia and was checked with the quiz questionnaire version 5.5. Results: In the recommendations section, experts deemed all items except "Omega-3 consumption" necessary. In the usability evaluation section, the items "Identifying the nearest emergency center, sending the patient’s geographic location to the doctor, finding the nearest medical center, and the possibility of changing color, and calculating BMI" were not confirmed. The usability evaluation of the application with an average score of 7.98±0.62 indicates a good level. Conclusion: The mobile-based recommender application can cause the patients to play a greater role in their treatment and acquire skills in the field of training after hospital discharge and lead to a reduction in re-hospitalization and cost.}, Keywords = {Mobile Health (mHealth), Heart Diseases, Cardiac Arrhythmia, Recommender Systems, Self-management}, volume = {10}, Number = {2}, pages = {165-174}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.22}, url = {http://jhbmi.ir/article-1-799-en.html}, eprint = {http://jhbmi.ir/article-1-799-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Askari, Elham and Motamed, Sara and AshoriGhaleKoli, Safour}, title = {Recognition of Alzheimer’s Patients in Emotional States Based on the Optimal Convolutional Neural Network and Electroencephalography}, abstract ={Introduction: Accurate diagnosis of Alzheimer’s disease in the early stages plays an important role in patient care, and preventive measures should be taken before irreversible brain damage occurs. With increasing age, there are changes in memory, which is normal, but the symptoms of Alzheimer’s disease are more than temporary forgetfulness. Early and intelligent diagnosis of Alzheimer’s disease in different situations can greatly help patients and physicians. Method: In the proposed method, a convolutional neural network will be used to improve the recognition of people with Alzheimer’s disease from healthy people in emotional states. First, the required pre-processing is done on the electroencephalography signal, and then, it will be applied as an input to the network. Next, the genetic algorithm is used to optimize the weights of the convolutional neural network. Results: The research shows that the frontal lobe of the brain is related to emotions and the use of F3 and F4 channels reflects more information compared to other channels, so with this information, the process of recognizing Alzheimer’s patients in emotional states is better. Conclusion: The proposed method was evaluated with other categories in valence and arousal states. It was observed that this method has a better efficiency compared to other methods with an accuracy of 92.3% in valence and 94.3% in arousal in recognizing people with Alzheimer’s disease.}, Keywords = {Alzheimer, Electroencephalography, Convolutional Neural Network, Genetic Algorithm}, volume = {10}, Number = {2}, pages = {175-184}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.23}, url = {http://jhbmi.ir/article-1-779-en.html}, eprint = {http://jhbmi.ir/article-1-779-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Moshiri, Fatemeh and Asosheh, Abbas}, title = {A Narrative Review of the Frameworks, Challenges, and Applications of Interoperability in E-Health}, abstract ={Introduction: Integrated Health Information Systems (HIS) are necessary to achieve national and international goals in health and development, which are facing cooperation problems due to diversity and heterogeneity. Interoperability as a key characteristic of data sharing and the main prerequisite of integration enables healthcare providers to make timely decisions for diagnosis, treatment, action, and patient care. Therefore, this research aimed to provide a category of interoperability concepts in electronic health (E-Health). Method: This research is a narrative review to categorize the interoperability concepts in E-Health. For this purpose, Scopus, Google Scholar, PubMed, Science Direct, and IEEE databases were searched using the keywords "Interoperability framework" and "Integration" with the combination of AND along with "E-Health". Therefore, all the articles referring to the concepts of interoperability were selected. Results: The result of the search in the aforementioned databases was 304 articles. By removing duplicate, out-of-reach, and unrelated articles, only 40 articles were examined, and topics related to the concepts of interoperability were classified based on six axes (dimensions, levels, obstacles, integration, architecture, and measurement). Finally, its applications and challenges were described in a narrative review. Conclusion: Due to the nature of the health system, integrity and intelligence are very important in this area. Therefore, to facilitate the exchange of information and the use of health information systems from each other’s services, paying attention to the presented classification has an undeniable effect, and also, an important role in the integrity and intelligence of the environment, and increases the efficiency and effectiveness in the quality of providing health care services.}, Keywords = {Interoperability, Integration, Intelligence, Software Architecture, Health Information Systems}, volume = {10}, Number = {2}, pages = {185-197}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.24}, url = {http://jhbmi.ir/article-1-748-en.html}, eprint = {http://jhbmi.ir/article-1-748-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {SarafiNejad, Afshi}, title = {Knowledge Discovery and Data Mining in Medical Sciences, a Review of the Requirements for Applied Researches}, abstract ={The article is a letter to the editor, so it has no abstract.}, Keywords = {Data Mining, Knowledge Discovery, Medical Informatics}, volume = {10}, Number = {2}, pages = {198-200}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.25}, url = {http://jhbmi.ir/article-1-819-en.html}, eprint = {http://jhbmi.ir/article-1-819-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Hassanzadeh, Parisa and Mohammadi, Mahnaz and Taheri, Sab}, title = {Investigating the Signaling Pathways Involved in Fighting Opportunistic Virus Infection with Helper T Lymphocyte Cells}, abstract ={Introduction: People with weakened immune system are more prone to opportunistic infections (OIs). Finding the association of these viruses and the transmission pathways associated with these viruses, such as EBV and CMV, play a significant role in investigating their association with helper T lymphocytes. In this study, bioinformatics analysis was used to examine and candidate genes in pathways associated with opportunistic viruses with helper T lymphocytes. Method: In this study, by referring to the GEO database, a suitable database was selected for analysis. This dataset included gene expression profiles in EBV and CMV virus infections. Gene clusters were classified into high and low expression. Rich databases such as Enrichr, STRING, and Networkanalyst were used to evaluate the data more accurately. Finally, the candidate genes were isolated and their protein binding was measured. Results: 964 high-expression genes and 837 low-expression genes are involved in the progression of opportunistic viral infections with lymphocytes. Cell cycle pathways, oxidative stress, RNA synthesis and TGFB were significantly observed. Conclusion: The present study showed that important proteins and genes played a major role in enhancing the inflammation of opportunistic viruses such as Epstein-Barr and Cytomegalovirus, among which CDK2, CCNB1, GSK3B, SRC, and SMAD3 showed a more prominent role in this pathway.}, Keywords = {Epstein Barr Virus, Gene Expression Profile, Bioinformatics Analysis, Cytomegalovirus, Opportunistic Viruses}, volume = {10}, Number = {3}, pages = {201-213}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.26}, url = {http://jhbmi.ir/article-1-776-en.html}, eprint = {http://jhbmi.ir/article-1-776-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {ShojaeiBaghini, Mahdie and MirzapourEstabragh, Fateme and Savas, Fateme}, title = {Feasibility Study of Telemedicine Implementation in Tourist Cities of Kerman Province}, abstract ={Introduction: The present study was conducted to evaluate the feasibility of telemedicine implementation in two cities in Kerman province with tourist attractions. Method: This descriptive cross-sectional study was conducted in 2021. Research data were collected using the standard questionnaire "Telemedicine Assessment Tool," designed by the Institute for Medicare (United States). Using the G-power software, the sample size of 68 people was determined. The researchers collected the required data in person at the hospitals under study. The collected data were analyzed using descriptive and analytical statistical methods through SPSS version 26. Results: According to demographic characteristics, the majority of people were women (78.7%), aged less than 30 years old (37.3%), with less than five years of work experience (40.0%), bachelor’s degree (88.0%), and clinical (57.3%). Regarding P-value, there is a significant difference between telemedicine services in terms of importance. Therefore, telemedicine services, consultation with specialists outside the hospital, patient referrals, and remote drug prescribing had respectively the highest priority. In contrast, changing the processes and structure of the hospital had the lowest priority. The lack of technical staff and the problems of initial costs were the main obstacles to the use of telemedicine, and the attitude of employees and competition were the least essential obstacles. Conclusion: Due to the tourism situation of the studied cities, hospitals must try to establish telemedicine. In this regard, investment to solve the problems of initial costs and technical staff has priority.}, Keywords = {Feasibility, Telemedicine, Tourism}, volume = {10}, Number = {3}, pages = {214-222}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.27}, url = {http://jhbmi.ir/article-1-765-en.html}, eprint = {http://jhbmi.ir/article-1-765-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Abidi, Atena and HeydaranDarooghehAmnyieh, zahra and Jamahmoodi, Hanieh and Salarniya, Setare and Zabbah, Im}, title = {Improving the Diagnosis of Arrhythmia using a Combination of Neural Networks in a Hierarchical Way}, abstract ={Introduction: Heart diseases are one of the most common types of diseases, which cause the death of many people. Arrhythmias are an irregular heartbeat that causes the heart to beat abnormally fast (tachycardia) or slow (bradycardia). Therefore, the identification and classification of cardiac arrhythmias using ECG signals is of great importance. This research aimed to provide a data mining-based model to improve the diagnosis of previous arrhythmia. Method: In this descriptive-analytical study, the UCI reference dataset, which consists of 452 samples with 279 features, was used. The samples were categorized into five classes for the detection and identification of different types of cardiac arrhythmias. The algorithm employed in this research is a combination of hierarchical neural networks (expert system combination). Results: In all networks, 70% of the samples were used for training, while the remaining 30% were used for testing. After modeling and comparing the generated models and recording the results, the prediction accuracy for cardiac arrhythmia in the absence of combination learning reached 89.5%, and it increased to 93.5% after employing the hierarchical expert combination approach. Conclusion: The results of this research show that the proposed method based on the combination of neural networks in a hierarchical form, which leads to the specialization of the task of each class, can have better performance compared to similar models in diagnosing cardiac arrhythmia.  }, Keywords = {Data Mining, Neural Networks, Cardiac Arrhythmia Diagnosis, Mixture of Experts}, volume = {10}, Number = {3}, pages = {223-237}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.28}, url = {http://jhbmi.ir/article-1-780-en.html}, eprint = {http://jhbmi.ir/article-1-780-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Pouramirarsalani, Shahrzad and Vahdanimanaf, Nader and Rajebi, Saman and Makouei, Somaye}, title = {Optimizing the KNN Algorithm to Diagnose Obstructive Pulmonary Diseases}, abstract ={Introduction: According to the World Health Organization, lung diseases are the third cause of death in the world. These diseases are chronic, so early diagnosis of these diseases is very important. Pulmonary function tests are important tools in examining and monitoring patients with respiratory injuries. This research aimed to optimize the K-Nearest Neighbor algorithm, which facilitates and accelerates self-assessment and interpretation of spirometry test results with higher accuracy. Method: In this study, a method is proposed that improves the limitations of the basic algorithm by optimizing, valuing features, and weighted voting. Using this method, obstructive pulmonary diseases are detected based on the data set of spirometry tests, and general parameters are classified into three categories, namely, asthma, chronic bronchitis, and emphysema. Results: In determining the appropriate method for calculating the data distance, the Minkowski method was chosen, and by applying the coefficients of the feature values, the accuracy of the classification increased. Weighted voting was done in the final part of the algorithm based on the Gaussian kernel, based on which a constant performance was obtained for changing the parameter of the number of neighbors. The results of the evaluations were carried out in the form of mutual validation. 95.4% accuracy and 93.2% precision were obtained in 3.12 seconds. Conclusion: The use of machine learning algorithms can be effective in the analysis of medical data. Therefore, in this study, these approaches were used to provide a new method of classification, so that the proposed algorithm could improve the basic method, and also, had better accuracy and performance than other previous methods.}, Keywords = {Classification, Obstructive Pulmonary Diseases, Fisher’s Discriminant Ratio, K Nearest Neighbor, Grasshopper Optimization}, volume = {10}, Number = {3}, pages = {238-259}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.29}, url = {http://jhbmi.ir/article-1-783-en.html}, eprint = {http://jhbmi.ir/article-1-783-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {RezazadehTamrin, Hanieh and Saniei, Elham and SalehiBarough, Mehdi}, title = {Breast Cancer Detection in Thermographic Images Using Hybrid Networks}, abstract ={Introduction: Breast cancer is the most common cancer in women that causes more deaths than other cancers. Thermography is one of the methods of breast cancer diagnosis. The most important challenge in early detection of these images can be human error or lack of access to a skilled person. The use of artificial intelligence methods in image processing can be effective in early detection and reduction of human error. The main aim of this research was to introduce hybrid networks for intelligent diagnosis of breast cancer from thermographic images. Method: The thermographic images used in this study were collected from the DMR-IR database. First, the main features of the images were extracted by deep convolutional network (CNN). Then, FCNNs and SVM algorithms were used to classify breast cancer from thermographic images. Results: The accuracy rate for CNN_FC and CNN-SVM algorithms was 94.2% and 0.95%, respectively. In addition, the reliability parameters for these classifiers were calculated as 92.1%, and 97.5%, and the sensitivity for each of these classifiers as 95.5%, and 94.1%, respectively. Conclusion: The proposed model based on the deep hybrid network has good accuracy compared to similar algorithms; therefore, it can help doctors in the early diagnosis of breast cancer through thermographic images and minimize human error.}, Keywords = {Diagnosis, Breast Cancer, Deep Learning, Convolutional Neural Network, Thermography}, volume = {10}, Number = {3}, pages = {260-268}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.30}, url = {http://jhbmi.ir/article-1-786-en.html}, eprint = {http://jhbmi.ir/article-1-786-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Sarvmeili, Javad and BaghbanKohnehrouz, Bahram and Gholizadeh, Ashraf and Ofoghi, Hamideh and Shanehbandi, Dariush}, title = {Introduction of an Efficient Multiepitopic Vaccine Against Different SARS-CoV-2 Strains: Reverse Vaccinology}, abstract ={Introduction: In recent years, COVID-19 has been recognized as a health threat. Despite vaccination, people still get the disease because the new variants have mutations in their genomes that allow them to bind to host receptors and evade the immune system’s responses. Therefore, the main aim of this study was to use bioinformatics tools to introduce a rapid and practical vaccine to fight against these diverse mutations in different SARS-CoV-2 strains. Method: To epitopes mapping, 32 different spike protein variants were retrieved. We then used the Immune Epitope Database (IEDB), NetCTL, and NetMHCIIpan to predict T and B cell epitopes. The vaccine based on protected epitopes was evaluated in terms of antigenicity, allergenicity, toxicity, solubility, physicochemical properties, population coverage, and secondary structure with relevant servers. Modeling using Robetta and docking with Toll-like receptor (TLR3) were performed using Cluspro, PatchDock, and FireDock, respectively. Results: After detailed evaluations, all the results confirmed the optimal quality of the vaccine. According to further investigations, this structure is similar to native proteins and there is a stable and strong interaction between the vaccine and the receptor. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. Conclusion: Based on the obtained results, the designed multi-epitope vaccine can serve as a prophylactic candidate against SARS-CoV-2.}, Keywords = {Dynamic Simulation, Immunoinformatics, Immune Simulation, Molecular Docking, Multi-epitope Vaccine}, volume = {10}, Number = {3}, pages = {269-293}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.31}, url = {http://jhbmi.ir/article-1-796-en.html}, eprint = {http://jhbmi.ir/article-1-796-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {NasiriKhoshroudi, Seyyed Hamzeh and Safaei, Ali Asghar and Soleimanjahi, Hoorieh}, title = {Conceptual Model and Requirements of An Electronic Case Report Form to Collect a Minimum Data Set of Complications of the COVID-19 Vaccine}, abstract ={Introduction: In recent years, the collection of data from the monitoring of clinical trials in the form of electronic case report forms (eCRF) has been increasingly used, because avoiding mistakes, reducing the duration of clinical studies, and preventing increasing data collection costs can affect the success of the study. Accordingly, this research aimed to present the conceptual model and requirements of the proposed eCRF, which is based on NoSQL database to achieve complications caused by the COVID-19 vaccine. Method: In this study, using the descriptive-applicative method, the conceptual model of the eCRF system was first designed to collect the minimum set of data on the complications of the COVID-19 vaccine. Then, the functional and non-functional requirements of the proposed system were identified using the methods of observation, thinking and reflection, and questionnaire. Results: This research has two main parts: the proposed conceptual model and the functional and non-functional requirements of the eCRF system. A conceptual model was proposed based on the components, modules, and objectives of the eCRF core, which can be used by researchers and clinical trial specialists. Requirements including the ability to register, insert, delete, and edit the case report form, were also calculated for four groups of patients, health care workers, disease experts, and the executive manager. Conclusion: The proposed system provides researchers and experts of clinical trials with classified and valid data regarding the registration of complications caused by the COVID-19 vaccine in an integrated manner. Therefore, some eCRF stakeholders such as patients, healthcare workers, disease experts, and executive managers and researchers can achieve a vaccine with acceptable safety and effectiveness by analyzing, interpreting, and sharing the recorded information.}, Keywords = {Electronic case report form, COVID-19, vaccination adverse effects, eCRF system requirements}, volume = {10}, Number = {3}, pages = {294-308}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.32}, url = {http://jhbmi.ir/article-1-813-en.html}, eprint = {http://jhbmi.ir/article-1-813-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Majdipoor, Leyla and Rahimi, Rezvan and Shahali, Shadab}, title = {The Effect of mHealth Interventions on Women\'s Health Outcomes: A Systematic Review of Randomized Controlled Trials}, abstract ={Introduction: Mobile health (mHealth) is an emerging technology with great potential for improving people's health. Due to its availability, this technology can create more motivation to improve women's health by providing safe, coordinated, and effective health care. Considering the importance of women's health, the present study was conducted to systematically investigate the impact of mHealth interventions on women's health outcomes. Method: The present study was conducted as a systematic review of clinical trial studies in English. PubMed, Scopus, Science Direct, the Cochrane Library, and ClinicalTrials.gov databases were reviewed according to the inclusion criteria from 2011 to 2021.  Results: A total of 28 articles were included in the study. mHealth tools, such as applications, text messages, voice or video calls, and wearable devices, were found to have multiple functions in this study, including providing information, reminders, education, and practical recommendations to improve the lifestyles of pregnant women in various areas. The study results indicated that in 82% of the studies, mHealth tools were effective in improving health outcomes for pregnant women, while in 18% of the studies, the intervention results did not differ from the standard method. Conclusion: mHealth interventions can have a positive effect on improving the health of pregnant women, including their lifestyle, knowledge, and attitude regarding their own and their baby's health. However, in all these studies, high heterogeneity limits the certainty of the evidence for these findings. Therefore, future studies should identify the context in which mHealth can be more effective for women's health.}, Keywords = {Mobile Health, Digital Health, Telehealth, Women's Health, Systematic Review}, volume = {10}, Number = {3}, pages = {309-325}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2023.33}, url = {http://jhbmi.ir/article-1-773-en.html}, eprint = {http://jhbmi.ir/article-1-773-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2023} } @article{ author = {Taghavizadeh, Mahsa and Nooshyar, Mehdi and Akbarimajd, Adel and Shahalinezhad, Sah}, title = {Automatic and Accurate Diagnosis of Alzheimer\'s Disease from MRI Images Improved by Deep Convolutional Neural Network}, abstract ={Introduction: Identifying and diagnosing Alzheimer's disease in brain tissue is one of the serious challenges in diagnosis in the field of medical image processing. Currently, MRI is the most common way to diagnose Alzheimer's disease, and failure to correctly identify the tissue involved in it can lead to incorrect diagnosis as healthy brain tissue. Deep learning algorithm as a process of detecting features related to damaged tissue and extracting useful information. In this research, we decided to use the convolutional neural network in the processing of medical images so that we can perform the diagnosis with better accuracy than the previous works. Methods: Using a convolutional neural network, the features of MRI images have been extracted. Alzheimer's images have been analyzed using Matlab2023a software and the intended outputs have been obtained. Results: Brain Alzheimer's images have been analyzed after pre-processing and entering the deep neural network, and in the output of the proposed algorithm, the identification accuracy and identification speed of the algorithm with the improvement of cloud parameters was higher compared to other common methods, which was 96% accuracy. presented in identification. Conclusion: The purpose of using deep learning is to make image data with large dimensions and a large number into a conceptual form for machines. It is expected that in the future feature extraction will be done more accurately and more details will be available to machine vision systems to recognize objects in the image.  }, Keywords = {Alzheimer's disease, convolutional neural network, deep learning, MRI images}, volume = {10}, Number = {4}, pages = {326-336}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.01}, url = {http://jhbmi.ir/article-1-812-en.html}, eprint = {http://jhbmi.ir/article-1-812-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Bagherian, Hossein and SaghaeiannejadIsfahani, Sakineh}, title = {Quality Assessment of the Information Technology (IT) Unit Services in Three Selected Hospitals in Isfahan City Using the Unweighted SERVPERF Model}, abstract ={Introduction: Considering the crucial role of IT unit in maintaining, preserving, and enhancing information systems and providing timely information to various individuals, the services provided by this unit must be of high quality. In this regard, the present study aimed to evaluate the quality of services provided by the Information Technology unit in three selected hospitals in Isfahan city using the SERVPERF model. Method: This is a quantitative and applied study. The statistical population consisted of all the employees who received service from the information technology unit in the educational hospitals of Isfahan city. The sample size was 351 people based on the Morgan’s table. The service quality of the information technology unit in the studied hospitals was investigated using the standard questionnaire of the SERVPERF model (non-weighted) in five areas. Results: The average of service quality of the information technology unit was 3.23, which had a statistically significant difference with the average value of 3 (P<0.05). The highest average is related to reliability (3.28) and responsiveness (3.25), respectively. The lowest average is related to the physical and tangible dimensions (3.14). Conclusion: The results show a negative gap in the service quality of information technology units in hospitals. To improve the quality of services in the dimensions of empathy, responsiveness, satisfaction, humility, goodwill, and information updates, regular training classes should be held for the employees of the information technology unit.}, Keywords = {Information Technology, Service Quality, Hospital, Evaluation, SERVPERF}, volume = {10}, Number = {4}, pages = {337-345}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.02 }, url = {http://jhbmi.ir/article-1-808-en.html}, eprint = {http://jhbmi.ir/article-1-808-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Hosseinpoor, Mohammadjav}, title = {Presenting an Intelligent Algorithm to Predict Heart Attack}, abstract ={Introduction: Today, heart attack is one of the most common causes of death in adults all over the world. According to the announcement of the Ministry of Health, Treatment, and Medical Education, 11 to 15 percent of deaths in Iran are caused by heart attacks, and in the world, Iran has the highest number of deaths due to heart disease in 2022. It has been estimated that deaths from these diseases will increase to 20 million people. Therefore, predicting this disease is one of the most challenging topics in the medical field, and today most prediction systems are created using artificial intelligence and machine learning algorithms. Method: This applied and descriptive research was conducted in 2022 using the information of 600 people who referred to Peymaniyeh and Motahari hospitals in Jahrom city. In this research, data were collected based on the available sources from both hospitals. To implement and evaluate the results, MATLAB programming language was used. Results: The parameters used in this research are among the classification parameters, including classification rate, precision, recall, and F-measure, which were obtained as 90.7, 90.2, 91.5, and 90.8, respectively. Conclusion: The results show that the proposed model will be able to predict the probability of stroke with a high percentage.}, Keywords = {Heart attack, Disease prediction, Intelligent model, Machine learning, Support vector machine}, volume = {10}, Number = {4}, pages = {346-356}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.03}, url = {http://jhbmi.ir/article-1-763-en.html}, eprint = {http://jhbmi.ir/article-1-763-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Ghasemi, Mahsa and Zamani, Javad and Minuchehr, Zarrin and Shamsara, Mehdi}, title = {In Silico Modeling of a Mutant Cas9 Protein for High-Fidelity Genome Editing}, abstract ={Introduction: CRISPR/Cas system is a bacterial-acquired immune system against viruses. spCas9 protein-derived from Streptococcus pyogenes is the most common Cas protein used in genome editing. The Cas9 proteins suffer from some problems like unwanted off-target cleavage, which is the major limitation in the application of the CRISPR system, especially for therapeutic issues. The present study aimed to in silico design of a mutant Cas protein to enhance the fidelity of the enzyme. Method: In this study, a new Cas9 protein was designed in silico via the selection of some mutations. The interaction of the mutant and wild-type Cas9 proteins with DNA was determined by molecular docking. Finally, the stability of the mutant and wild-type proteins was evaluated using molecular dynamics. Results: The outcome of this study resulted in designing a mutant Cas9 protein bioinformatically containing 5 amino acid substitutions. Molecular docking scores for the wild-type and mutant proteins were -1073.86 and -349.41, respectively. Conclusion: Molecular docking indicated that the number of hydrogen bonds between protein and DNA reduced in the mutant state. Moreover, based on the molecular dynamic findings, the stability of both mutant and wild-type Cas9 proteins was similar.}, Keywords = {High-fidelity Cas9, Molecular dynamic, Molecular docking, CRISPR/Cas, Protein engineering, Gene editing}, volume = {10}, Number = {4}, pages = {357-366}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.04}, url = {http://jhbmi.ir/article-1-838-en.html}, eprint = {http://jhbmi.ir/article-1-838-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Aliakbarpour, Hamideh and Shahmansoury, Ashraf and Bazaee, Ghasemali}, title = {Identifying and Prioritizing the Implementation Steps of the Blockchain Technology Roadmap in Health-Oriented Organizations Using the Fuzzy Delphi Method}, abstract ={Introduction: A Roadmap is a tool to help organizations facilitate the successful implementation of any technology. Then, this study aimed to identify and prioritize the stages of blockchain technology roadmap implementation in health-oriented organizations. Method: To refine and evaluate the appropriateness of steps extracted from the subject literature, the fuzzy Delphi method was used. Access to the most reliable group agreement of experts on a specific issue is done using a questionnaire and asking experts’ opinions, often according to their feedback. Results: After identifying and extracting the factors through the study of various articles and localization and determining the importance of the variables, by applying the weight limit in the model, the level of experts’ agreement with each of the components was obtained and their suggested and corrective points were divided. The resulting absolute mean indicates the intensity of experts’ agreement with each research factor. Conclusion: The results of the survey of experts’ views show that the priorities of monitoring technology, identifying the application of blockchain technology in the organization, identifying structural, technical, legal, and financial challenges, designing, removing governance and upstream obstacles, appropriate capital transfer, cooperation between old and new infrastructure, and increasing membership, high importance and priorities of basic training, identifying the benefits of digital transformation and competitive advantage, internal and external stakeholders, and expanding the business model to other sectors are of lower importance.}, Keywords = {Blockchain, Blockchain roadmap, Fuzzy Delphi method, Blockchain in health}, volume = {10}, Number = {4}, pages = {367-379}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.05}, url = {http://jhbmi.ir/article-1-821-en.html}, eprint = {http://jhbmi.ir/article-1-821-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Bahador, Fatemeh and Javanmard, Zohreh and Sabahi, Azam and Salehi, Fatemeh and Shahabi, Shirin and FatehBahari, Rez}, title = {Determining the Minimum Data Set for Electronic Health Records of Tuberculosis Patients}, abstract ={Introduction: The minimum data set is considered an important tool in the registration systems and monitoring programs, which provides the possibility of collecting and transferring data at the country level. Considering the importance of tuberculosis in Iran and especially the high incidence of this disease in the marginal provinces of the country, the present study was designed and implemented to determine the minimum data set for the electronic health records of tuberculosis patients. Method: This descriptive study was conducted in two phases. In the first phase, a comprehensive review was conducted in PubMed, Science Direct, SID, and Magiran databases to identify data elements. The data were collected using the data extraction form and polled using the Delphi method. Descriptive statistics and SPSS version 16 were used for data analysis. Results: The minimum data set of tuberculosis was divided into two categories: administrative data with five classes and 35 data elements, and clinical data with five classes and 43 data elements. Conclusion: The minimum data set of tuberculosis is an effective tool for managing and recording the information of patients suffering from this disease. The results of this study can help researchers and developers in the process of designing and implementing an integrated information system for registering the information of patients with tuberculosis.}, Keywords = {Minimum Data Set, Tuberculosis, Electronic Health Record}, volume = {10}, Number = {4}, pages = {380-385}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = { 10.34172/jhbmi.2024.06}, url = {http://jhbmi.ir/article-1-829-en.html}, eprint = {http://jhbmi.ir/article-1-829-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Mortezaali, Sara and Zarrabi, Mahboobeh}, title = {The Study of the Binding Ability of Antimicrobial Peptides to Spike Protein of the Coronavirus by Docking and Molecular Dynamics Simulation}, abstract ={Introduction: Since the start of 2020, SARS-CoV-2 has infected a significant number of individuals, prompting extensive research. Antimicrobial peptides can be considered as a promising treatment for emerging viral pathogens due to their safety, efficacy, and specificity. Method: In this study, first, 104 natural antiviral peptides were chosen from APD databases.  Then, the third structure of proteins was modeled by PEP-FOLD 3 server. The structures were refined and optimized for docking operation. Subsequently, peptide-protein docking was performed using AutoDock Vina. The most favorable peptide-protein complex, chosen based on binding energy, was employed for molecular simulation using GROMACS. The simulation was carried out for 100 ns at 310° K and pH=7. Gromos54a7 force field was used in this study and the SPC water model was used as solvent. Results: The obtained results from molecular dynamics examine the stability of complex structure and energy calculations. RMSD, RMSF, radius of gyration, and SASA analyses indicate the stability of the peptide-protein complex during the simulation. Additionally, LJ, CL, HBond, and ΔG analyses were conducted to calculate the energy interactions between the peptide and the Spike protein. Conclusion: The results indicate that antimicrobial peptides can effectively bind to the SARS-CoV-2 spike protein, acting as inhibitors with a favorable binding energy. Consequently, these peptides can be employed for therapeutic and experimental studies of COVID-19.}, Keywords = {Antimicrobial peptide, Spike protein, Molecular dynamics simulation, Docking, COVID-19}, volume = {10}, Number = {4}, pages = {386-399}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.07}, url = {http://jhbmi.ir/article-1-836-en.html}, eprint = {http://jhbmi.ir/article-1-836-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} } @article{ author = {Janati, Asma and JafariShahbazzadeh, Mehdi}, title = {The role of Artificial Intelligence in the Diagnosis of Chronic Diseases}, abstract ={The article is a letter to the editor, so it does not have an abstract.}, Keywords = {Artificial intelligence, Health}, volume = {10}, Number = {4}, pages = {400-402}, publisher = {دانشگاه علوم پزشکی کرمان}, doi = {10.34172/jhbmi.2024.08}, url = {http://jhbmi.ir/article-1-852-en.html}, eprint = {http://jhbmi.ir/article-1-852-en.pdf}, journal = {Journal of Health and Biomedical Informatics}, issn = {2423-3870}, eissn = {2423-3498}, year = {2024} }