دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 9 3 2022 12 1 Accuracy Improvement of Mood Disorders Prediction using a Combination of Data Mining and Meta-Heuristic Algorithms 106 119 FA Mana Fariborzi M.Sc. in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran maanaafariborzi@gmail.com N 0000-0002-1349-0198 Masoomeh Zeinalnezhad Ph.D. in Industrial Engineering, Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran m.zeinalnezhad@gmail.com Y 0000-0001-6767-1157 Abbas Saghaei Ph.D. in Industrial Engineering, Professor, Department of Industrial Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran N 10.34172/jhbmi.2022.01 Introduction: Since the delay or mistake in the diagnosis of mood disorders due to the similarity of their symptoms hinders effective treatment, this study aimed to accurately diagnose mood disorders including psychosis, autism, personality disorder, bipolar, depression, and schizophrenia, through modeling and analyzing patients' data. Method: Data collected in this applied developmental research included 996 records with 130 features obtained by interviewing and completing questionnaires in a mental hospital in the city of Sari, Iran in 2021. After preprocessing, the number of features was reduced to 91, and then through Principal Component Analysis (PCA) reduced to 35 factors.  Modeling was done in Python software with K-Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms. The models were evaluated to select algorithms with higher accuracy. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to determine the optimal parameters of the selected algorithms. Results: Among the machine learning algorithms, random forest with 91% accuracy and support vector machine with 90% accuracy showed better performance. The genetic algorithms did not make any notable increase in prediction accuracy. Whereas considering N=30, T=150, W=0.9, c1=2, and c2=2 in the particle swarm optimization algorithm increased the prediction accuracy up to 3.3 %. Conclusion: With less classification error compared to similar studies, the PSO-SVM model designed in this study can be used in patient data monitoring with acceptable accuracy and can be used in intelligent systems in psychiatric centers. Mood Disorder, Machine Learning, Meta-Heuristic Algorithms, Data Mining, Prediction http://jhbmi.ir/article-1-712-en.html http://jhbmi.ir/article-1-712-en.pdf

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 9 3 2022 12 1 Identifying the Challenges of the Blockchain Technology Maturity Model in Health-Oriented Organizations 120 129 FA Hamideh Aliakbarpour PhD Student in Information Technology Management, Faculty of Management, Islamic Azad University, South Tehran Branch, Tehran, Iran aliakbarpour@arakmu.ac.ir N 0000-0002-7495-7978 Ashraf Shahmansoury PhD in Business Administration, Assistant Professor, South Tehran Branch, Islamic Azad University, Tehran, Iran shahmansouryashrf@gmail.com Y 0000-0002-0519-1198 Ghasemali Bazaee PhD in Management and Administrative Sciences, Assistant Professor, Central Tehran Branch, Islamic Azad University, Tehran, Iran ghasemalibazaee@gmail.com N 0000-0002-7915-4074 10.34172/jhbmi.2022.02 Introduction: Blockchain is a widely used technology in the health area; however, it also comes with challenges. By identifying these challenges, the road to blockchain maturity can be made smoother in this field. This study aimed to identify the challenges of the blockchain technology maturity model in health-oriented organizations. Method: In this phenomenological qualitative study, experts in the field of blockchain technology were the statistical population 12 of whom were selected to conduct an in-depth interview. The validity of the findings was guaranteed by the methods of matching by members and peer review. In this manner, the participants reviewed the process of primary data (interview results) analysis. And in the peer review procedure, supervisors, advisors, and two doctoral students commented on the findings. To analyze the data, open and axial coding was used. Results: The challenges of this model, in the technical and organizational sections, have been drawn and presented in the form of a structural model. In the resulting model, each of the categories is specified with its subset, and their frequency is also specified in the model to prioritize and determine their importance. Conclusion: In health-oriented organizations, the challenges of information security in the technology sector and transparency in the organizational structure are among the most important challenges. The security of data, due to the high importance of maintaining them, and creating transparency, due to the impossibility of manipulating data, are challenges that if ignored, the process of reaching technology maturity in health-oriented organizations will fail. Risks and Challenges, Blockchain Technology Maturity Model, Health-Oriented Organizations, Blockchain Technology, Phenomenology http://jhbmi.ir/article-1-699-en.html http://jhbmi.ir/article-1-699-en.pdf

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 9 3 2022 12 1 Investigating the Information Technology Competency of Nurses in Critical Care Units: A cross-sectional study 130 137 FA Jamileh Farokhzadian PhD in Nursing, Associate Professor, Nursing Research Center, Kerman University of Medical Sciences, Kerman, Iran farokhzadian2010@yahoo.com N 0000-0002-9621-3486 Somayeh Jouparinejad MSc in Nursing, Shafa hospital, Kerman University of Medical Sciences, Kerman, Iran N Hojjat Farahmandnia PhD in Health in Disasters and Emergencies, Assistant Professor, Health in Disasters and Emergencies Research Center, Future Research Institute in Health, Kerman University of Medical Sciences, Kerman, Iran hojat.sheikhbardsiri@gmail.com Y 0000-0002-3264-6792 10.34172/jhbmi.2022.03 Introduction: With the rapid growth of information technology (IT) and the expectations of health care environments, the presence of nurses with informatics skills in the health care environment to effectively use and manage health care information in health care delivery seems necessary; therefore, this study was conducted to evaluate the IT competency of special care nurses from their point of view. Method: This cross-sectional study aimed to investigate the IT competency of nurses in the special care departments of Afzalipur Hospital of Kerman University of Medical Sciences in 1400 SH. The study population included 60 nurses working in the intensive care units (ICU and CCU) who were included in the study using a census method. The Nursing Informatics Competency Assessment Tool (NICAT) was used in this research. Results: The findings of the study showed that the average score of IT qualification of nurses in the special care department (2.70 ± 0.85) is within the range of “competent”. The data showed that the highest score of IT competency of the studied nurses was attributed to the computer literacy section (2.82±0.93), and the lowest score was related to the information management skills section (2.58±0.85). Conclusion: It is suggested that clinical nursing managers improve the IT competency of nurses by implementing educational programs in the field of “IT literacy in the hospital”, especially in the division of information management skills which had the lowest average score, so that they can use the latest evidence to provide more effective care at the bedside.   Technology Competency, Information, Nursing, Critical Care Units http://jhbmi.ir/article-1-732-en.html http://jhbmi.ir/article-1-732-en.pdf

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 9 3 2022 12 1 Impact of the Mobile App “Cognogene” on University Students\' Communication Skills 138 147 FA Seyed Mostafa Hosseini PhD in Molecular Genetics, Human Genetics Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran smhosseini@bmsu.ac.ir N 0000-0003-3716-8133 Somaieh Borjalilu PhD and Post-doctorate in Psychology, Assistant Professor, Department of Psychology, Shahid Behashti University, Tehran, Iran sborjalilu@gmail.com Y 0000-0001-7348-0672 10.34172/jhbmi.2022.04 Introduction: The notion of electronic mental health care is suggested in today's world. With the aid of this care, people's talents are no longer limited by time or location, and those in need of mental health care can get these services by downloading and installing software on their mobile devices. The Cognogene application software has been created to provide electronic mental health services on the smartphone platform. This study aimed to measure the impact of the mobile app “Cognogene” on university students' communication skills. Method: The statistical population of this study included 104 students studying at Tehran University in the winter semester (2021-2022). They were randomly divided into two 54-member groups (experimental and control). The experimental group received electronic psychological education (consisting of short video clips, daily homework, motivational letters, etc.) in 21 sessions, while the control group did not. To collect data, Queen Dom's communication skills questionnaire was used in two stages: pre-test and post-test. The data were analyzed using the one-way analysis of variance. Results: Results showed that the intervention made a significant difference in the mean score of all components of the study of communication skills except the components of "receiving and sending messages" and "emotional control". Conclusion: This finding suggests that training through the Cognogene application can be used as a complement to other training methods to improve communication skills Electronic Health, Communication Skills, Cognogene http://jhbmi.ir/article-1-717-en.html http://jhbmi.ir/article-1-717-en.pdf

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 9 3 2022 12 1 Hypertension Prediction in Primary School Students Using an Ensemble Machine Learning Method 148 157 FA Reza Besharati PhD in Health Care Management, Assistant Professor, Department of Nursing, Kashmar Branch, Islamic Azad University, Kashmar, Iran rezabesharati@ymail.com N 0000-0001-9875-8217 Hamidreza Tahmasbi PhD in Computer Engineering, Assistant Professor, Department of Computer Engineering, Kashmar Branch, Islamic Azad University, Kashmar, Iran htahma@gmail.com Y 0000-0001-9875-8271 10.34172/jhbmi.2022.05 Introduction: The prevalence of hypertension in children is increasing, and this complication is considered the most important risk factor for cardiovascular diseases in older age. Early detection and control of hypertension can prevent its progress and reduce its consequences. Machine learning methods can help predict this complication promptly and reduce cost and time. This study aimed to provide a model based on ensemble machine learning methods to more accurately predict the hypertension of primary school children. Method: This is an applied developmental study that was conducted using the information of 1287 primary school children aged 7-13 years in Kashmar city. After data preprocessing, to achieve a more accurate diagnosis of hypertension in children, the output results of five common machine learning methods in disease diagnosis including decision tree, naive Bayesian, nearest neighbors, artificial neural network, and support vector machine using weighted majority voting method were combined. Results: The results showed that the accuracy, sensitivity, and specificity of the proposed model were 90.31%, 80.65%, and 93.54%, respectively, and compared to similar studies it performed better. Conclusion: The proposed model can better predict and diagnose hypertension in children and improve accuracy and reduce the error rate. This model can be a useful and early tool in the diagnosis of hypertension in children, reducing the consequences and costs of this complication and being a big step in the fight against hypertension. Hypertension, Primary School Students, Machine Learning Methods, Prediction http://jhbmi.ir/article-1-723-en.html http://jhbmi.ir/article-1-723-en.pdf

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 9 3 2022 12 1 Clinical decision supports for brain-dead kidney allocation: a review study of requirements, challenges, and solutions 158 179 FA Marziye Moniri MSc Student of Medical Informatics, Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran m.moniri73@yahoo.com N 0000-0001-9183-354X Zahra Niazkhani PhD in Medical Informatics, Associate Professor, Nephrology and Kidney Transplant Research Center, Urmia University of Medical Sciences, Urmia, Iran niazkhani.z@umsu.ac.ir N 0000-0003-2662-9731 Khadijeh Makhdoomi Subspecialist in Nephrology, Associate Professor, Nephrology and Kidney Transplant Research Center, Urmia University of Medical Sciences, Urmia, Iran makhdoomikhadijeh@yahoo.com N 0000-0002-4503-0612 Parviz Rashidi Khazaee PhD in Information Technology, Assistant Professor, Faculty of Information Technology and Computer Engineering, Urmia University of Technology, Urmia, Iran P.rashidi@uut.ac.ir N 0000-0001-8498-0058 Habibollah Pirnejad PhD in Medical Informatics, Associate Professor, Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran pirnejad.h@umsu.ac.ir Y 0000-0001-9284-5702 10.34172/jhbmi.2022.06 Introduction: With the change in the epidemiology of diseases, chronic kidney disease has shown a significant increase worldwide. Kidney transplantation is the optimal treatment option to improve patient quality of life. The main challenge in benefiting from a brain-dead patient's kidney is the selection of a right recipient in limited time. The purpose of this study was to review the requirements for designing and implementing clinical decision support systems for allocating brain-dead kidneys and the challenges facing them. Method: This scoping review was conducted by searching Scopus, PubMed, and Google scholar to find full-text English articles published until the end of 2021. The articles were analyzed based on the objectives of the study, the factors used in decision-making for allocation, the algorithms used in the decision-making process, and their outcome evaluations. Results: Two categories of medical (including the quality of the donated kidney) and equity/fairness factors were used in most allocation algorithms. However, the number of factors, the method, and the order of their application were different in different studies. Studies have reported different challenges but a positive impact using decision support systems. Conclusion: One of the main challenges in the proper management of kidney transplants from brain-dead donors is to have local, agreed-upon protocols and algorithms regarding the methods, order, and weight of each of the medical and equity factors in the proposed protocol. Algorithms based on scoring have mostly been favored. Therefore, the probability of using a decision support system on this base seems to be higher.   kidney transplant, kidney allocation system, cadaver donor, clinical decision support system, scoring system, donor-recipient matching system, review study http://jhbmi.ir/article-1-716-en.html http://jhbmi.ir/article-1-716-en.pdf

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 9 3 2022 12 1 A Narrative Review of Blockchain in Healthcare: Applications and challenges 180 192 FA Zahra Anvari Sadi M.Sc. Student of Medical Informatics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran zahra_anvari@modares.ac.ir N Aliasghar Safaei Associate Professor, PhD in Computer Engineering (Software), Department of Medical Informatics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran aa.safaei@modares.ac.ir Y 0000-0003-1985-8720 10.34172/jhbmi.2022.07 Introduction: Healthcare as an industry has unique requirements such as patient security and privacy, interoperability, sharing, transmission, and access control of patient data. On the other hand, the advantages of blockchain technology and the compliance of these advantages with the requirements of the health industry have encouraged researchers to investigate the methods of applying blockchain in healthcare. The rapid increase in blockchain and health research has created many applications. Despite the high potential of this technology for health applications, there are still challenges to be reviewed. In this article, we provide a narrative overview of blockchain applications and challenges in the healthcare industry. Method: In this narrative review, published studies until October 2022 that were accessible in PubMed, IEEE Xplore, Web of Science, and Scopus databases were searched; from 254 related studies, 171 were identified by their titles, and finally, after applying inclusion and exclusion criteria, 30 articles were selected to be reviewed. Results: The results showed that 10 applications of blockchain technology in healthcare are as follows: safe sharing of health data, establishing electronic medical records, medical record tracking, opioid prescription tracking, deep learning, drug supply chain, clinical trials, COVID-19 pandemic management, and remote patient monitoring. The application of blockchain technology in healthcare also has challenges such as interoperability, security and privacy, immutability, scalability, patient engagement, transparency, and confidentiality. Conclusion: The compatibility between the requirements of the health industry and the characteristics of the blockchain has created a suitable platform for the use of this technology in the health industry. However, there are challenges in the path of these applications that must be solved.   Blockchain, Healthcare, Applications and Challenges, Narrative Review http://jhbmi.ir/article-1-728-en.html http://jhbmi.ir/article-1-728-en.pdf

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 2423-3498 9 3 2022 12 1 Artificial Intelligence in Healthcare 193 195 FA Amirhosein Nabizadeh PhD in Artificial Intelligence Institute of Systems and Computer Engineering, Research and Development (INESC-ID, Lisbon, Portugal)/ Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran hossein.nabizadeh.r@gmail.com Y 10.34172/jhbmi.2022.08 The article is a letter to the editor, so it has no abstract. Artificial Intelligence, Healthcare http://jhbmi.ir/article-1-749-en.html http://jhbmi.ir/article-1-749-en.pdf