دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 7 1 2020 6 1 Developing an Application for Screening and Determining Severity of Autism Spectrum Disorder in Primary Health Care Centers 1 9 FA Nayere Atamanesh M.Sc. in Health Information Technology, Health Information Management Dept., School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran Mostafa langarizadeh Ph.D. in Medical Informatics, Associate Professor, Health Information Management Dept., School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran Mitra Hakim shooshtrai Specialist in Pediatric Psychiatry, Associate Professor, Mental Health Research Center, School of Behavioral Sciences and Mental Health (Tehran Institute of Psychiatry), Iran University of Medical Sciences, Tehran, Iran 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.

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 7 1 2020 6 1 Design and Implementation of a Fuzzy Intelligent System for Predicting Mortality in Trauma Patients in the Intensive Care Unit 10 19 FA Mitra Montazeri M.Sc. in Artificial Intelligent, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Mahdi Ahmadinejad Fellowship of Intensive Care Medicine, Associate Professor, Anesthesiology Computer Dept., Faculty of Medicine, Kerman University of Medical Sciences, Kerman, Iran Mahdieh Montazeri Ph.D. Student in Medical Informatics, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Mohadeseh Montazeri Ph.D. Student in Computer Software, Lecturer, Computer Dept., Technical and Vocational University, Kerman, Iran 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.

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 7 1 2020 6 1 Predicting Survival of Patients with Lung Cancer Using Improved Adaptive Neuro-Fuzzy Inference System 20 29 FA Ommolbanin Abbasi M.Sc. Student in Computer Engineering, Department of Computer Engineering, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran Mohammadreza Ramezanpour Assistant Professor, Department of Computer Engineering, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran Reihaneh Khorsand Assistant Professor, Department of Computer Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran 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.

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 7 1 2020 6 1 Improving biological activity prediction of protein kinase inhibitors using artificial neural network and partial least square methods 30 39 FA Roya Arian M.Sc. in Bioelectrics, Bioelectrics Dept., School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran Alireza Mehri Dehnavi Ph.D. in Bioelectrics, Professor, Bioelectrics Dept., School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran Fahimeh Ghasemi Ph.D. in Bioelectrics, Assistant Professor, Bioinformatics Dept., School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran 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

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 7 1 2020 6 1 Assessing the Impact of Health Service Providers and Recipients on the Adoption of E-health Services 40 51 FA MohammadTaghi Taghavifard Ph.D. in Industrial Engineering, Associate Professor, Industrial Management Dept., Faculty of Management & Accounting, Allameh Tabataba'i University, Tehran, Iran Akbar Yousefvand Ph.D. Student in Information Technology Management, School of Graduate Studies, Allameh Tabataba'i University, Tehran, Iran 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

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 7 1 2020 6 1 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) 52 59 FA Akram Ghanizadeh M.Sc. of Community-Based Education in Health System, Bam University of Medical Sciences, Bam, Iran MohammadReza Rajabalipour M.Sc. of Health Education, Instructor, Public Health Dept., School of Health, Bam University of Medical Sciences, Bam, Iran Roghayeh Ershad Sarabi Ph.D. of Health Information Management, Assistant Professor, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran 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.

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 7 1 2020 6 1 A Graph-Based Clustering Approach to Identify Cell Populations in Single-Cell RNA Sequencing Data 60 72 FA Amin Einipour Ph.D. Student in Computer Engineering, Computer Engineering Dept., Faculty of Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran Mohammad Mosleh Ph.D. in Computer Engineering, Assistant Professor, Computer Engineering Dept., Faculty of Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran Karim Ansari-Asl Ph.D. in Biomedical Engineering, Associate Professor, Electrical Engineering Dept., Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran 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.

دانشگاه علوم پزشکی کرمان Journal of Health and Biomedical Informatics 2423-3870 7 1 2020 6 1 The Content and Structure of Electronic Personal Health Records: A Systematic Review 73 90 FA Esmaeel Toni M.Sc. Student in Medical Informatics, Health Information Technology Dept., School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran Habibollah Pirnejad Ph.D. in Medical Informatics, Associate Professor, Patient Safety Research Center and Health Information Technology Dept., School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran Zahra Niazkhani Ph.D. in Medical Informatics, Associate Professor, Nephrology and Kidney Transplant Research Center and Health Information Technology Dept., School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran 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.