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ارائه برنامه کاربردی غربالگری و تعیین شدت اختلال طیف اتیسم در سطح یک خدمات بهداشتی و درمانی Developing an Application for Screening and Determining Severity of Autism Spectrum Disorder in Primary Health Care Centers 1 1 مقدمه: اختلال طیف اتیسم، مجموعه‌ای از اختلالات رشد عصبی است که علاوه بر کودک، تمامی اعضای خانواده نیز درگیر می‌شوند. این اختلال بر اساس مشاهده رفتارها و فعالیت‌های عملکردی کودک قابل تشخیص است. غربالگری و تعیین شدت به موقع باعث افزایش توانمندی‌های کودک می‌گردد. هدف اصلی پژوهش ایجاد برنامه کاربردی غربالگری و تعیین شدت اختلال طیف اتیسم در سطح یک خدمات بهداشتی و درمانی بود. روش: این پژوهش از نوع توسعه‌ای-کاربردی بود. به منظور تعیین اقلام داده، پرسشنامه‌ای تهیه و از پنج نفر از پزشکان متخصص روانپزشکی اطفال نیازسنجی انجام شد. بر اساس نتایج، نمونه اولیه برنامه کاربردی مبتنی بر تلفن همراه هوشمند طراحی گردید. سپس برای ارزیابی کاربردپذیری در اختیار 30 نفر از بهورزان شاغل در سطح یک مراکز خدمات بهداشتی و درمانی قرار گرفت. تحلیل داده‌ها با استفاده از روش آمار توصیفی و در محیط نرم‌افزار SPSS نسخه 16 صورت گرفت. نتایج: پزشکان متخصص شرکت‌کننده، تمامی عناصر داده‌ای پرسشنامه را ضروری دانستند. پنج حیطه برقراری ارتباط، حرکات درشت، حرکات ظریف، حل مسئله، شخصی اجتماعی، سؤالات کلی و تعیین شدت ابتلاء در نرم افزار در نظر گرفته شد که هر یک از عناوین دارای زیرمجموعه کامل بودند. نتایج ارزیابی از برنامه نشان داد که بهورزان برنامه را در سطح خوب ارزیابی کردند. نتیجه ­گیری: می‌توان از برنامه‌های کاربردی مبتنی بر موبایل جهت کمک به بهورزان در غربالگری و تعیین شدت زودهنگام اتیسم در جهت افزایش فعالیت‌های عملکردی و شناختی کودکان و کاهش مراجعات به مراکز تخصصی‌تر غربالگری و تعیین شدت استفاده نمود. 2 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. 1 9 2019/10/12 1398/7/20 2020/02/23 1398/12/4 نیره عطامنش Nayere Atamanesh کارشناس ارشد فناوری اطلاعات سلامت، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران n.atamanesh@yahoo.com مصطفی لنگری زاده Mostafa langarizadeh دکتری تخصصی انفورماتیک پزشکی، دانشیار، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران Langarizadeh.m@iums.ac.ir میترا حکیم شوشتری Mitra Hakim shooshtrai فوق تخصص روانپزشکی کودک، دانشیار، مرکز تحقیقات بهداشت روان، دانشکده علوم رفتاری و سلامت روان (انستیتو روانپزشکی تهران)، دانشگاه علوم پزشکی ایران، تهران، ایران ایران mitra.hakim@gmail.com Autism Spectrum Disorder Smartphone Application Screening Determining Severity اختلال طیف اتیسم تلفن همراه هوشمند برنامه کاربردی غربالگری تعیین شدت 1. Al Mamun KA, Bardhan S, Ullah MA, Anagnostou E, Brian J, Akhter S, Rabbani MG. Smart autism—A mobile, interactive and integrated framework for screening and confirmation of autism. Conf Proc IEEE Eng Med Biol Soc 2016;2016:5989-92. doi: 10.1109/EMBC.2016.7592093.##2. Mintz J. Additional key factors mediating the use of a mobile technology tool designed to develop social and life skills in children with Autism Spectrum Disorders: Evaluation of the 2nd HANDS prototype. Computers & Education 2013;63:17-27. https://doi.org/10.1016/j.compedu.2012.11.006##3. American Psychiatric Association. What Is Autism Spectrum Disorder? [cited 12 Dec 2019]. Available from: https://www.psychiatry.org/patients-families/autism/what-is-autism-spectrum-disorder##4. Pilling S, Baron-Cohen S, Megnin-Viggars O, Lee R, Taylor C, Guideline Dev G. Recognition, referral, diagnosis, and management of adults with autism: summary of NICE guidance. BMJ 2012;344:e4082. doi: 10.1136/bmj.e4082.##5. Esler AN, Bal VH, Guthrie W, Wetherby A, Ellis Weismer S, Lord C. The Autism Diagnostic Observation Schedule, Toddler Module: Standardized Severity Scores. J Autism Dev Disord 2015;45(9):2704‐20. doi:10.1007/s10803-015-2432-7##6. Goldin RL, Matson JL, Beighley JS, Jang J. Autism spectrum disorder severity as a predictor of Battelle Developmental Inventory–Second Edition (BDI-2) scores in toddlers. Dev Neurorehabil 2014;17(1):39-43. doi: 10.3109/17518423.2013.839585.##7. Brookman-Frazee L, Stahmer AC. Effectiveness of a multi-level implementation strategy for ASD interventions: Study protocol for two linked cluster randomized trials. Implement Sci 2018;13(1):66. doi: 10.1186/s13012-018-0757-2. ##8. Elder JH, Kreider CM, Brasher SN, Ansell M. Clinical impact of early diagnosis of autism on the prognosis and parent-child relationships. Psychol Res Behav Manag 2017;10:283‐92. doi:10.2147/PRBM.S117499##9. Vanvuchelen M, Van Schuerbeeck L, Braeken MA. Screening accuracy of the parent-completed Ages and Stages Questionnaires–second edition as a broadband screener for motor problems in preschoolers with autism spectrum disorders. Autism 2017;21(1):29-36. doi:10.1177/1362361315621703.##10. Santosh P, Tarver J, Gibbons F, Vitoratou S, Simonoff E. Protocol for the development and validation of a questionnaire to assess concerning behaviours and mental health in individuals with autism spectrum disorders: the Assessment of Concerning Behaviour (ACB) scale. BMJ Open 2016;6(3):e010693. http://dx.doi.org/10.1136/bmjopen-2015-010693##11. Baribeau DA, Anagnostou E. An Update on Medication Management of Behavioral Disorders in Autism. Curr Psychiatry Rep 2014;16(3):437. doi: 10.1007/s11920-014-0437-0.##12. Bright Futures Steering Committee, Medical Home Initiatives for Children With Special Needs Project Advisory Committee. Identifying infants and young children with developmental disorders in the medical home: An algorithm for developmental surveillance and screening. Pediatrics 2006;118(1):405-20. doi: 10.1542/peds.2006-1231.##13. Windham GC, Smith KS, Rosen N, Anderson MC, Grether JK, Coolman RB, et al. Autism and developmental screening in a public, primary care setting primarily serving Hispanics: Challenges and results. J Autism Dev Disord 2014;44(7):1621-32. doi: 10.1007/s10803-014-2032-y.##14. Antezana L, Scarpa A, Valdespino A, Albright J, Richey JA. Rural Trends in Diagnosis and Services for Autism Spectrum Disorder. Front Psychol 2017;8:590. doi:10.3389/fpsyg.2017.00590.##15. Elder JH, Brasher S, Alexander B. Identifying the barriers to early diagnosis and treatment in underserved individuals with autism spectrum disorders (ASD) and their families: A qualitative study. Issues Ment Health Nurs 2016;37(6):412-20. doi: 10.3109/01612840.2016.1153174.##16. Shepherd D, Landon J, Goedeke S, Ty K, Csako R. Parents’ assessments of their child’s autism-related interventions. Research in Autism Spectrum Disorders. 2018;50:1-0. https://doi.org/10.1016/j.rasd.2018.02.005##17. Samadi SA, McConkey R. Screening for autism in Iranian preschoolers: Contrasting M-CHAT and a scale developed in Iran. J Autism Dev Disord 2015;45(9):2908-16. doi: 10.1007/s10803-015-2454-1.##18. Seneviratne MG, Hersch F, Peiris DP. HealthNavigator: a mobile application for chronic disease screening and linkage to services at an urban Primary Health Network. Aust J Prim Health 2018;24(2):116-22. doi: 10.1071/PY17070.##19. Kagohara DM, van der Meer L, Ramdoss S, O'Reilly MF, Lancioni GE, Davis TN, et al. Using iPods (R) and iPads (R) in teaching programs for individuals with developmental disabilities: A systematic review. Res Dev Disabil 2013;34(1):147-56. doi: 10.1016/j.ridd.2012.07.027.##20. Mehdizadeh H, Ayatollahi H, Esmaeili N, Kamkar M. Designing and building a teledermatology system. J Mazandaran Univ Med Sci 2015;25(123):170-84. [In Persian]##21. Bangerter A., Manyakov NV, Lewin D, Boice M, Skalkin A, Jagannatha S, et al. Caregiver Daily Reporting of Symptoms in Autism Spectrum Disorder: Observational Study Using Web and Mobile Apps. JMIR Ment Health 2019;6(3):e11365. doi:10.2196/11365##22. Kanne SM, Carpenter LA, Warren Z. Screening in toddlers and preschoolers at risk for autism spectrum disorder: Evaluating a novel mobile‐health screening tool. Autism Res 2018;11(7):1038-49. doi: 10.1002/aur.1959.##23. Campbell K, Carpenter KL, Espinosa S, Hashemi J, Qiu Q, Tepper M, Calderbank R, Sapiro G, Egger HL, Baker JP, Dawson G. Use of a digital Modified Checklist for Autism in Toddlers–Revised with follow-up to improve quality of screening for autism. J Pediatr 2017;183:133-9.e1. doi: 10.1016/j.jpeds.2017.01.021.##1. Al Mamun KA, Bardhan S, Ullah MA, Anagnostou E, Brian J, Akhter S, Rabbani MG. Smart autism—A mobile, interactive and integrated framework for screening and confirmation of autism. Conf Proc IEEE Eng Med Biol Soc 2016;2016:5989-92. doi: 10.1109/EMBC.2016.7592093.##2. Mintz J. Additional key factors mediating the use of a mobile technology tool designed to develop social and life skills in children with Autism Spectrum Disorders: Evaluation of the 2nd HANDS prototype. Computers & Education 2013;63:17-27. https://doi.org/10.1016/j.compedu.2012.11.006##3. American Psychiatric Association. What Is Autism Spectrum Disorder? [cited 12 Dec 2019]. Available from: https://www.psychiatry.org/patients-families/autism/what-is-autism-spectrum-disorder##4. Pilling S, Baron-Cohen S, Megnin-Viggars O, Lee R, Taylor C, Guideline Dev G. Recognition, referral, diagnosis, and management of adults with autism: summary of NICE guidance. BMJ 2012;344:e4082. doi: 10.1136/bmj.e4082.##5. Esler AN, Bal VH, Guthrie W, Wetherby A, Ellis Weismer S, Lord C. The Autism Diagnostic Observation Schedule, Toddler Module: Standardized Severity Scores. J Autism Dev Disord 2015;45(9):2704‐20. doi:10.1007/s10803-015-2432-7##6. Goldin RL, Matson JL, Beighley JS, Jang J. Autism spectrum disorder severity as a predictor of Battelle Developmental Inventory–Second Edition (BDI-2) scores in toddlers. Dev Neurorehabil 2014;17(1):39-43. doi: 10.3109/17518423.2013.839585.##7. Brookman-Frazee L, Stahmer AC. Effectiveness of a multi-level implementation strategy for ASD interventions: Study protocol for two linked cluster randomized trials. Implement Sci 2018;13(1):66. doi: 10.1186/s13012-018-0757-2. ##8. Elder JH, Kreider CM, Brasher SN, Ansell M. Clinical impact of early diagnosis of autism on the prognosis and parent-child relationships. Psychol Res Behav Manag 2017;10:283‐92. doi:10.2147/PRBM.S117499##9. Vanvuchelen M, Van Schuerbeeck L, Braeken MA. Screening accuracy of the parent-completed Ages and Stages Questionnaires–second edition as a broadband screener for motor problems in preschoolers with autism spectrum disorders. Autism 2017;21(1):29-36. doi:10.1177/1362361315621703.##10. Santosh P, Tarver J, Gibbons F, Vitoratou S, Simonoff E. Protocol for the development and validation of a questionnaire to assess concerning behaviours and mental health in individuals with autism spectrum disorders: the Assessment of Concerning Behaviour (ACB) scale. BMJ Open 2016;6(3):e010693. http://dx.doi.org/10.1136/bmjopen-2015-010693##11. Baribeau DA, Anagnostou E. An Update on Medication Management of Behavioral Disorders in Autism. Curr Psychiatry Rep 2014;16(3):437. doi: 10.1007/s11920-014-0437-0.##12. Bright Futures Steering Committee, Medical Home Initiatives for Children With Special Needs Project Advisory Committee. Identifying infants and young children with developmental disorders in the medical home: An algorithm for developmental surveillance and screening. Pediatrics 2006;118(1):405-20. doi: 10.1542/peds.2006-1231.##13. Windham GC, Smith KS, Rosen N, Anderson MC, Grether JK, Coolman RB, et al. Autism and developmental screening in a public, primary care setting primarily serving Hispanics: Challenges and results. J Autism Dev Disord 2014;44(7):1621-32. doi: 10.1007/s10803-014-2032-y.##14. Antezana L, Scarpa A, Valdespino A, Albright J, Richey JA. Rural Trends in Diagnosis and Services for Autism Spectrum Disorder. Front Psychol 2017;8:590. doi:10.3389/fpsyg.2017.00590.##15. Elder JH, Brasher S, Alexander B. Identifying the barriers to early diagnosis and treatment in underserved individuals with autism spectrum disorders (ASD) and their families: A qualitative study. Issues Ment Health Nurs 2016;37(6):412-20. doi: 10.3109/01612840.2016.1153174.##16. Shepherd D, Landon J, Goedeke S, Ty K, Csako R. Parents’ assessments of their child’s autism-related interventions. Research in Autism Spectrum Disorders. 2018;50:1-0. https://doi.org/10.1016/j.rasd.2018.02.005##17. Samadi SA, McConkey R. Screening for autism in Iranian preschoolers: Contrasting M-CHAT and a scale developed in Iran. J Autism Dev Disord 2015;45(9):2908-16. doi: 10.1007/s10803-015-2454-1.##18. Seneviratne MG, Hersch F, Peiris DP. HealthNavigator: a mobile application for chronic disease screening and linkage to services at an urban Primary Health Network. Aust J Prim Health 2018;24(2):116-22. doi: 10.1071/PY17070.##19. Kagohara DM, van der Meer L, Ramdoss S, O'Reilly MF, Lancioni GE, Davis TN, et al. Using iPods (R) and iPads (R) in teaching programs for individuals with developmental disabilities: A systematic review. Res Dev Disabil 2013;34(1):147-56. doi: 10.1016/j.ridd.2012.07.027.##20. Mehdizadeh H, Ayatollahi H, Esmaeili N, Kamkar M. Designing and building a teledermatology system. J Mazandaran Univ Med Sci 2015;25(123):170-84. [In Persian]##21. Bangerter A., Manyakov NV, Lewin D, Boice M, Skalkin A, Jagannatha S, et al. Caregiver Daily Reporting of Symptoms in Autism Spectrum Disorder: Observational Study Using Web and Mobile Apps. JMIR Ment Health 2019;6(3):e11365. doi:10.2196/11365##22. Kanne SM, Carpenter LA, Warren Z. Screening in toddlers and preschoolers at risk for autism spectrum disorder: Evaluating a novel mobile‐health screening tool. Autism Res 2018;11(7):1038-49. doi: 10.1002/aur.1959.##23. Campbell K, Carpenter KL, Espinosa S, Hashemi J, Qiu Q, Tepper M, Calderbank R, Sapiro G, Egger HL, Baker JP, Dawson G. Use of a digital Modified Checklist for Autism in Toddlers–Revised with follow-up to improve quality of screening for autism. J Pediatr 2017;183:133-9.e1. doi: 10.1016/j.jpeds.2017.01.021. ##

طراحی و پیاده‌سازی یک سیستم هوشمند فازی جهت پیش‌بینی درصد موارد مرگ و میر بیماران تروما در بخش مراقبت‌های ویژه Design and Implementation of a Fuzzy Intelligent System for Predicting Mortality in Trauma Patients in the Intensive Care Unit 1 1 مقدمه: بخش ICU بیمارستان یکی از بخش‌های پر هزینه در بخش سلامت ملی می‌باشد. این هزینه‌ها تا حد زیادی به مدت اقامت بیمار وابسته است. لذا پیش‌بینی طول مدت اقامت بیماران و درصد موارد مرگ‌و‌میر در بخش مراقبت‌های ویژه اهمیت زیادی دارد. لذا در این پژوهش به طراحی سیستم هوشمند مبتنی بر منطق فازی جهت پیش‌بینی درصد موارد مرگ‌و‌میر بیماران تروما در بخش مراقبت‌های ویژه پرداخته شد. روش: داده‌های مورد نیاز برای طراحی سیستم، از پرونده‌های بیماران از سال 91 - 1389 گردآوری شد، سپس سیستم با استفاده از داده‌های گردآوری شده از هر پرونده اجرا شد و میزان همخوانی تشخیص سیستم با تشخیص نهایی ثبت شده در پرونده بیمار مقایسه گردید. مدل پیشنهادی نروفازی با 5 مدل هوشمند دیگر مقایسه گردید. این مقایسه بر اساس حساسیت، دقت، ویژگی و سطح زیر منحنی راک محاسبه و ارزیابی گردید. نتایج: میزان دقت این 6 مدل حدوداً به ترتیب 83% و 81‌%،80%، 75%، 82% و 81% است. نتیجه­ گیری: مدل نروفازی بهترین مدل ارزیابی شد و دارای بالاترین میزان دقت است. از نظر سطح زیر منحنی ROC مجدداً این مدل بیشترین سطح زیر منحنی را دارد؛ لذا به‌کارگیری مدل نروفازی در زمینه تشخیص و پیش‌بینی درصد موارد مرگ و میر بیماران تروما در بخش مراقبت‌های ویژه پیشنهاد می‌شود. این امر در تحقیقات مرتبط با حوزه سلامت و به خصوص در تخصیص منابع درمانی برای افرادی که پرمخاطره پیش‌بینی می‌شوند از اهمیت بالایی برخوردار است. 2 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. 10 19 2019/10/122018/11/21 1397/8/30 2020/02/232019/12/11 1398/9/20 میترا منتظری Mitra Montazeri کارشناسی ارشد هوش مصنوعی، مرکز تحقیقات انفورماتیک پزشکی، پژوهشکده آینده‌پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران mitra.montazeri2@gmail.com مهدی احمدی نژاد Mahdi Ahmadinejad فلوشیپ مراقبت‌های ویژه، دانشیار‌، گروه بیهوشی، دانشگاه علوم پزشکی کرمان‌، کرمان، ایران ایران mehdia50@gmail.com مهدیه منتظری Mahdieh Montazeri دانشجوی دکترای انفورماتیک پزشکی، مرکز تحقیقات انفورماتیک پزشکی، پژوهشکده آینده‌پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران mahdiehmontazeri@yahoo.com محدثه منتظری Mohadeseh Montazeri دانشجوی دکترای نرم افزار کامپیوتر، مربی، بخش کامپیوتر، دانشکده حضرت فاطمه (س)، دانشگاه فنی و حرفه‌ای، کرمان، ایران ایران mohadeseh_montazeri@yahoo.com Trauma Patients Intensive Care Unit Classification Prediction Intelligent Models ANFIS Naïve Bayes Trees Random Forest 1NN AdaBoost SVM بیماران تروما بخش مراقبت‌های ویژه دسته‌بندی پیش‌بینی مدل‌های هوشمندANFIS Naïve Bayes Trees Random Forest 1NN AdaBoost SVM 1. 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پیش‌بینی بقاء بیماران مبتلا به سرطان ریه با استفاده از سیستم استنتاج عصبی- فازی تطبیقی بهبودیافته Predicting Survival of Patients with Lung Cancer Using Improved Adaptive Neuro-Fuzzy Inference System 1 1 مقدمه: سرطان ریه منبع اصلی مرگ‌ومیر برای مردان و زنان در سراسر جهان می‌باشد. بیماری ریه توسعه و رشد غیرقابل‌کنترل سلول‌ها در یک یا هر دو ریه می‌باشد. تشخیص زودرس سرطان آسان نیست؛ اما اگر سریع تشخیص داده شود، قابل‌درمان است. هدف از این مطالعه، ساخت مدل بهینه پیش‌بینی کننده بقاء بیماران مبتلابه سرطان ریه بر اساس ویژگی‌های بیماران با رویکرد داده‌کاوی می‌باشد. روش: در این مطالعه توصیفی- کاربردی، از الگوریتم سیستم استنتاج عصبی فازی تطبیقیANFIS  و الگوریتم بهینه‌سازی ازدحام ذرات PSO برای پیش‌بینی بقاء بیماران مبتلابه سرطان ریه استفاده شد. در این مطالعه، از پایگاه داده معتبر برنامه‌ نظارت، اپیدمی‌شناسی و نتایج نهایی SEER دانشگاه لوییزول آمریکا استفاده‌ شد. برای ارزیابی روش پیشنهادی از معیارهای دقت،  صحت، خطا و جذر خطای میانگین مربعات استفاده شد. نتایج: نتایج نهایی به‌ دست‌آمده در این مطالعه نشان‌دهنده برتری روش بهینه‌سازی ANFIS با الگوریتم PSO نسبت به سایر روش‌ها، در راستای پیش‌بینی بقاء بیماران مبتلابه سرطان ریه با متوسط صحت برابر 99/80% برای بقاء یک‌ساله، 99/74%  برای بقاء دوساله و 99/66%  برای بقا پنج‌ساله بر روی مجموعه داده SEER بود. نتیجه ­گیری: استفاده از مدل بهینه‌سازی شده ANFIS با الگوریتم PSO در پیش‌بینی بقاء بیماران مبتلا به سرطان ریه بسیار قدرتمند است. مدل پیشنهادی نسبت به سایر مدل‌های مورد مقایسه دارای  بیشترین صحت، دقت و کمترین میزان خطا بوده است؛ بنابراین به‌کارگیری ایـن مـدل درزمینه پیش‌بینی بقا پیشنهاد می‌شود. 2 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. 20 29 2019/10/122018/11/212019/10/27 1398/8/5 2020/02/232019/12/112020/02/23 1398/12/4 ام البنین عباسی Ommolbanin Abbasi دانشجوی کارشناسی ارشد مهندسی کامپیوتر، گروه مهندسی کامپیوتر، واحد مبارکه، دانشگاه آزاد اسلامی، اصفهان، ایران ایران daryaes2014@gmail.com محمدرضا رمضان پور Mohammadreza Ramezanpour استادیار، گروه مهندسی کامپیوتر، واحد مبارکه، دانشگاه آزاد اسلامی، اصفهان، ایران ایران mr.ramezanpoor@gmail.com ریحانه خورسند Reihaneh Khorsand استادیار، گروه مهندسی کامپیوتر، واحد دولت آباد، دانشگاه آزاد اسلامی، اصفهان، ایران ایران r.khorsand@iauda.ac.ir Data Mining Survival Prediction Lung Cancer Survival Rate داده‌کاوی پیش‌بینی بقا سرطان ریه نرخ بقاء 1. Levy A, Hendriks LE, Berghmans T, Faivre-Finn C, GiajLevra M, GiajLevra N, et al. EORTC Lung Cancer Group Survey on the Definition of NSCLC Synchronous Oligometastatic Disease. Eur J Cancer 2019;122:109-14. doi: 10.1016/j.ejca.2019.09.012. ##2. Fathi-hajiabadi F, Farzi S. Prediction of lung cancer in Kermanshah province based on artificial neural network, 1st National Conference of New Outlook on Electrical and Computer Engineering; 2017 Mar 19-20; Kermanshah: Islamic Azad University Kermanshah Branch; 2017. [In Persian] ##3. Aliferis CF, Tsamardinos I, Massion PP, Statnikov AR, Fananapazir N, Hardin DP. Machine Learning Models for Classification of Lung Cancer and Selection of Genomic Markers Using Array Gene Expression Data. American Association for Artificial Intelligence; 2003. ##4. Win SL, Htike ZZ, Yusof F, Noorbatcha IA. Gene expression mining for predicting survivability of patients in early stages of lung cancer. International Journal on Bioinformatics & Biosciences 2014;4(2): 1-9. doi:10.5121/ijbb.2014.4201##5. Karhan Z, Tunç T. Lung Cancer Detection and Classification with Classification Algorithms. IOSR Journal of Computer Engineering (IOSR-JCE) 2016;18(6):71-7.##6. Yu D, Zhou M, Yang F, Dong D, Gevaert O, Liu Z, et al. Convolutional neural networks for predicting molecular profiles of non-small cell lung cancer. 14th International Symposium on Biomedical Imaging; 2017 Apr 18-21; (Melbourne, VIC, Australia: IEEE; 2017. p. 69-572. doi: 10.1109/ISBI.2017.7950585##7. Murty NR, Babu MP. A Critical Study of Classification Algorithms for LungCancer Disease Detection and Diagnosis. International Journal of Computational Intelligence Research 2017;13(5):1041-8.##8. Wang Z, Feng F, Zhou X, Duan L, Wang J, Wu Y, et al. Development of diagnostic model of lung cancer based on multiple tumor markers and data mining. Oncotarget. 2017;8(55):94793-804. doi: 10.18632/oncotarget.21935.##9. Mirabbasi SE, Barzegarinejad A. Predicting Survival Cancer Patients After Surgery Using Data Mining Techniques. National Conference on Knowledge and Technology of Engineering Sciences of Iran; 2017 Mar 10; Tehran: Farzanegan Institute of Higher Education; 2017. [In Persian] ##10. Lynch CM, Abdollahi B, Fuqua JD, Alexandra R, Bartholomai JA, Balgemann RN, et al. Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int J Med Inform 2018; 108: 1-8. doi: 10.1016/j.ijmedinf.2017.09.013##11. Banay M, Sajedi H. Predicting Survival of Patients with Lung Cancer Using Classification Algorithms in Data Mining. 2nd National Conference on Computer Science and Information Technology, Najafabad: Islamic Azad University Najafabad Branch; 2015. [In Persian] ##12. SEER Research Data Record Description Cases Diagnosed in 1973-2014 [cited 2017 May 20]. Available from: https://seer.cancer.gov/data-software/documentation/seerstat/nov2016/TextData.FileDescription.pdf ##13. Dormishi A, Ataei M, Khaloo-Kakaie R, Mikaeil R, Shaffiee-Haghshenas S. Performance evaluation of gang saw using hybrid ANFIS-DE and hybrid ANFIS-PSO algorithms. Journal of Mining and Environment 2019; 10(2):543-57. doi: 10.22044/JME.2018.6750.1496##14. Esfe MH. Thermal Conductivity Modeling of Aqueous CuO Nanofluids by Adaptive Neuro-Fuzzy Inference System (ANFIS) Using Experimental Data. Periodica Polytechnica Chemical Engineering 2018; 62(2): 202-8. doi.org/10.3311/PPch.9670##15. Gholami A, Bonakdari H, Ebtehaj I, Gharabaghi B, Khodashenas SR, Talesh SH, et al. A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS, Engineering Geology, 2018: 298-309. doi.org/10.1016/j.enggeo.2018.03.030##16. Kadir T, Gleeson F. Lung cancer prediction using machine learning and advanced imaging techniques. Transl Lung Cancer Res 2018;7(3):304‐12. doi:10.21037/tlcr.2018.05.15##17. Karaboga D, Kaya E. Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review 2018;52(4):1-31. doi: 10.1007/s10462-017-9610-2##18. Jiang L, Huang W, Liu J, Harris K, Yarmus L, Shao W, Chen H, et al. Endosonography With Lymph Node Sampling for Restaging the Mediastinum in Lung Cancer: A Systematic Review and Pooled Data Analysis. J Thorac Cardiovasc Surg. 2020;159(3):1099-108.e5. doi: 10.1016/j.jtcvs.2019.07.095.##19. Ahmadi M, Ramezanpour M, Khorsand R. Diagnosis of Liver Disorders Using a Combination of Adaptive NeuronFuzzy Inference System and Particle Swarm Optimization Algorithm. Health Inf Manage 2019; 16(3): 115-21. [In Persian] doi: 10.22122/him.v16i3.3886## ##

بهبود تخمین اثر بیولوژیکی ملکول‌های مهارکننده پروتئین کیناز، با استفاده از شبکه عصبی و مینیمم خطای جزئی Improving biological activity prediction of protein kinase inhibitors using artificial neural network and partial least square methods 1 1 مقدمه: پروتئین کیناز عامل ایجاد بسیاری از بیماری‌ها از جمله سرطان است؛ بنابراین مهار آن ها در درمان بسیاری از بیماری‌ها نقش بسزایی ایفا می‌کند. کشف داروهای جدید با روش‌های آزمایشگاهی، از جمله موضوعات هزینه بردار و زمان‌بر می‌باشد؛ یافتن مدل‌های محاسباتی قابل اطمینان برای شناسایی مهار‌کننده‌ها می‌تواند هزینه‌ها را به حداقل برساند. هدف از این مطالعه به کار‌گیری روش شبکه عصبی جهت طبقه‌بندی ترکیبات در دو گروه فعال و غیر فعال و مدل رگرسیون خطی مینیمم خطای جزئی به منظور تخمین میزان اثر بیولوژیکی آن‌ها است. روش: در این پژوهش، پس از استخراج توصیفگرها از داده‌ها، به منظور جلوگیری از بیش برازش مدل‌ها، کاهش ابعاد داده از طریق الگوریتم ژنتیک صورت پذیرفت. همچنین جهت طبقه‌بندی داده‌ها در کلاس فعال و غیر فعال از مدل شبکه عصبی و جهت تخمین مقادیر اثر بیولوژیکی ریزملکول‌ها از مدل رگرسیون خطی مینیمم خطای جزئی استفاده شد. نتایج: نتایج نشان داد بعد از کاهش بعد توصیفگرهای ملکولی، صحت مدل شبکه عصبی از 74/45%  به 86/7% تغییر یافت. این مدل در تعداد گره‌های لایه پنهان برابر با 6، صحت 86/7%، حساسیت 83/4%، اختصاصی بودن 89/6% و ضریب همبستگی متیو 73/2% را ارائه می‌دهد. مدل رگرسیون خطی مینیمم خطای جزئی نیز با میزان همبستگی متوسط 85/8% مقادیر بیولوژیکی را تخمین می‌زند. نتیجه‌گیری: مدل طبقه‌بندی شبکه عصبی و مدل رگرسیون خطی مینیمم خطای جزئی تا میزان قابل قبولی می‌توانند مهارکننده‌های پروتئین کیناز را پیش‌بینی کنند و الگوریتم کاهش بعد ژنتیک عملکرد این مدل‌ها را بهبود می‌بخشد. 2 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 30 39 2019/10/122018/11/212019/10/272019/04/22 1398/2/2 2020/02/232019/12/112020/02/232019/11/2 1398/8/11 رویا آرین Roya Arian کارشناسی ارشد بیوالکتریک، گروه بیوالکتریک، دانشکده فناوری‌های نوین علوم پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران ایران royaarian101@yahoo.com علیرضا مهری دهنوی Alireza Mehri Dehnavi دکترای تخصصی بیوالکتریک، استاد، گروه بیوالکتریک، دانشکده فناوری‌های نوین علوم پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران ایران mehri@med.mui.ac.ir فهیمه قاسمی Fahimeh Ghasemi دکترای تخصصی بیوالکتریک، استادیار‌، گروه بیوانفورماتیک، دانشکده فناوری‌های نوین علوم پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران ایران f_ghasemi@amt.mui.ac.ir Protein kinase Classification Neural network Regression partial least square پروتئین کیناز طبقه‌بندی شبکه عصبی رگرسیون مینیمم خطای جزئی 1. Lu Z, Hunter T. Metabolic kinases moonlighting as protein kinases. Trends Biochem Sci 2018;43(4):301-10. doi: 10.1016/j.tibs.2018.01.006. ##2. Ghasemi F, Mehridehnavi A, Fassihi A, Pérez-Sánchez H. Deep neural network in QSAR studies using deep belief network. Applied Soft Computing. 2018;62:251-8. doi.org/10.1016/j.asoc.2017.09.040##3. Mostashari-Rad T, Arian R, Sadri H, Mehridehnavi A, Mokhtari M, Ghasemi F, et al. Study of CXCR4 chemokine receptor inhibitors using QSPR and molecular docking methodologies. Journal of Theoretical and Computational Chemistry 2019;18(04):1950018. doi: 10.1142/S0219633619500184##4. Hughes ZE, Walsh TR. Structural Disruption of an Adenosine-Binding DNA Aptamer on Graphene: Implications for Aptasensor Design. ACS Sens. 2017;2(11):1602-11. doi: 10.1021/acssensors.7b00435.##5. Tahir M, Hayat M, Kabir M. Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition. Computer Methods and Programs in Biomedicine 2017;146:69-75. doi.org/10.1016/j.cmpb.2017.05.008##6. Tiwari AK, Srivastava R. An Efficient Approach for Prediction of Nuclear Receptor and Their Subfamilies Based on Fuzzy k-Nearest Neighbor with Maximum Relevance Minimum Redundancy. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 2018;88(1):129-36. doi: 10.1007/s40010-016-0325-6##7. Derevyanko G, Grudinin S, Bengio Y, Lamoureux G. Deep convolutional networks for quality assessment of protein folds. Bioinformatics 2018;34(23):4046-53. doi: 10.1093/bioinformatics/bty494##8. Geng H, Lu T, Lin X, Liu Y, Yan F. Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier. Biochemistry Research International 2015:1-7.##9. Ghasemi F, Mehridehnavi A, Fassihi A, Pérez-Sánchez H. Deep neural network in QSAR studies using deep belief network. Applied Soft Computing 2018;62:251-8. doi.org/10.1016/j.asoc.2017.09.040##10. Song J, Li F, Takemoto K, Haffari G, Akutsu T, Chou KC, Webb GI. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. Journal of Theoretical Biology. 2018;443:125-37. doi.org/10.1016/j.asoc.2017.09.040##11. Wei L, Xing P, Tang J, Zou Q. PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only. IEEE Trans Nanobioscience 2017;16(4):240-7. doi: 10.1109/TNB.2017.2661756.##12. Alladio E, Giacomelli L, Biosa G, Corcia DD, Gerace E3, Salomone A, et al. Development and validation of a Partial Least Squares-Discriminant Analysis (PLS-DA) model based on the determination of ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs) in hair for the diagnosis of chronic alcohol abuse. Forensic Sci Int 2018;282:221-30. doi: 10.1016/j.forsciint.2017.11.010.##13. Alghamedy F, Bopaiah J, Jones D, Zhang X, Weiss HL, Ellingson SR. Incorporating Protein Dynamics Through Ensemble Docking in Machine Learning Models to Predict Drug Binding. AMIA Jt Summits Transl Sci Proc 2018;2017:26‐34. ##14. Saghaie L, Shahlaei M, Madadkar-Sobhani A, Fassihi A. Application of partial least squares and radial basis function neural networks in multivariate imaging analysis-quantitative structure activity relationship: study of cyclin dependent kinase 4 inhibitors. Journal of Molecular Graphics and Modelling. 2010;29(4):518-28. doi.org/10.1016/j.jmgm.2010.10.001##15. Ghasemi F, Fassihi A, Pérez-Sánchez H, Mehri Dehnavi A. The role of different sampling methods in improving biological activity prediction using deep belief network. J Comput Chem 2017;38(4):195-203. doi: 10.1002/jcc.24671.##16. Mauri A, Consonni V, Pavan M, Todeschini R. Dragon software: An easy approach to molecular descriptor calculations. MATCH Communications in Mathematical and in Computer Chemistry 2006;56(2):237-48.##17. Fang J, Yang R, Gao L, Zhou D, Yang S, Liu AL, et al. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. J Chem Inf Model 2013;53(11):3009-20. doi: 10.1021/ci400331p. ##18. Maltarollo VG, Kronenberger T, Espinoza GZ, Oliveira PR, Honorio KM. Advances with support vector machines for novel drug discovery. Expert Opin Drug Discov 2019;14(1):23-33. doi: 10.1080/17460441.2019.1549033.##19. Korkmaz S, Zararsiz G, Goksuluk D. MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development. PLoS One 2015;10(4):e0124600. doi: 10.1371/journal.pone.0124600. ##20. Ahmadi M, Shahlaei M. Quantitative structure-activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods. Res Pharm Sci 2015;10(4):307-25. ##21. Shahlaei M, Bahrami G, Abdolmaleki S, Sadrjavadi K. Majnooni MB. Application of unfolded principal component analysis–radial basis function neural network for determination of celecoxib in human serum by three-dimensional excitation–emission matrix fluorescence spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2015; 138.675-83. doi.org/10.1016/j.saa.2014.12.007##22. Shahlaei M, Madadkar-Sobhani A, Fassihi A, Saghaie L, Arkan E. QSAR study of some CCR5 antagonists as anti-HIV agents using radial basis function neural network and general regression neural network on the basis of principal components. Medicinal Chemistry Research 2012;21(10):3246-62. doi: 10.1007/s00044-011-9863-2##1. Lu Z, Hunter T. Metabolic kinases moonlighting as protein kinases. Trends Biochem Sci 2018;43(4):301-10. doi: 10.1016/j.tibs.2018.01.006. ##2. Ghasemi F, Mehridehnavi A, Fassihi A, Pérez-Sánchez H. Deep neural network in QSAR studies using deep belief network. Applied Soft Computing. 2018;62:251-8. doi.org/10.1016/j.asoc.2017.09.040##3. Mostashari-Rad T, Arian R, Sadri H, Mehridehnavi A, Mokhtari M, Ghasemi F, et al. Study of CXCR4 chemokine receptor inhibitors using QSPR and molecular docking methodologies. Journal of Theoretical and Computational Chemistry 2019;18(04):1950018. doi: 10.1142/S0219633619500184##4. Hughes ZE, Walsh TR. Structural Disruption of an Adenosine-Binding DNA Aptamer on Graphene: Implications for Aptasensor Design. ACS Sens. 2017;2(11):1602-11. doi: 10.1021/acssensors.7b00435.##5. Tahir M, Hayat M, Kabir M. Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition. Computer Methods and Programs in Biomedicine 2017;146:69-75. doi.org/10.1016/j.cmpb.2017.05.008##6. Tiwari AK, Srivastava R. An Efficient Approach for Prediction of Nuclear Receptor and Their Subfamilies Based on Fuzzy k-Nearest Neighbor with Maximum Relevance Minimum Redundancy. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 2018;88(1):129-36. doi: 10.1007/s40010-016-0325-6##7. Derevyanko G, Grudinin S, Bengio Y, Lamoureux G. Deep convolutional networks for quality assessment of protein folds. Bioinformatics 2018;34(23):4046-53. doi: 10.1093/bioinformatics/bty494##8. Geng H, Lu T, Lin X, Liu Y, Yan F. Prediction of Protein-Protein Interaction Sites Based on Naive Bayes Classifier. Biochemistry Research International 2015:1-7.##9. Ghasemi F, Mehridehnavi A, Fassihi A, Pérez-Sánchez H. Deep neural network in QSAR studies using deep belief network. Applied Soft Computing 2018;62:251-8. doi.org/10.1016/j.asoc.2017.09.040##10. Song J, Li F, Takemoto K, Haffari G, Akutsu T, Chou KC, Webb GI. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. Journal of Theoretical Biology. 2018;443:125-37. doi.org/10.1016/j.asoc.2017.09.040##11. Wei L, Xing P, Tang J, Zou Q. PhosPred-RF: A Novel Sequence-Based Predictor for Phosphorylation Sites Using Sequential Information Only. IEEE Trans Nanobioscience 2017;16(4):240-7. doi: 10.1109/TNB.2017.2661756.##12. Alladio E, Giacomelli L, Biosa G, Corcia DD, Gerace E3, Salomone A, et al. Development and validation of a Partial Least Squares-Discriminant Analysis (PLS-DA) model based on the determination of ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs) in hair for the diagnosis of chronic alcohol abuse. Forensic Sci Int 2018;282:221-30. doi: 10.1016/j.forsciint.2017.11.010.##13. Alghamedy F, Bopaiah J, Jones D, Zhang X, Weiss HL, Ellingson SR. Incorporating Protein Dynamics Through Ensemble Docking in Machine Learning Models to Predict Drug Binding. AMIA Jt Summits Transl Sci Proc 2018;2017:26‐34. ##14. Saghaie L, Shahlaei M, Madadkar-Sobhani A, Fassihi A. Application of partial least squares and radial basis function neural networks in multivariate imaging analysis-quantitative structure activity relationship: study of cyclin dependent kinase 4 inhibitors. Journal of Molecular Graphics and Modelling. 2010;29(4):518-28. doi.org/10.1016/j.jmgm.2010.10.001##15. Ghasemi F, Fassihi A, Pérez-Sánchez H, Mehri Dehnavi A. The role of different sampling methods in improving biological activity prediction using deep belief network. J Comput Chem 2017;38(4):195-203. doi: 10.1002/jcc.24671.##16. Mauri A, Consonni V, Pavan M, Todeschini R. Dragon software: An easy approach to molecular descriptor calculations. MATCH Communications in Mathematical and in Computer Chemistry 2006;56(2):237-48.##17. Fang J, Yang R, Gao L, Zhou D, Yang S, Liu AL, et al. Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. J Chem Inf Model 2013;53(11):3009-20. doi: 10.1021/ci400331p. ##18. Maltarollo VG, Kronenberger T, Espinoza GZ, Oliveira PR, Honorio KM. Advances with support vector machines for novel drug discovery. Expert Opin Drug Discov 2019;14(1):23-33. doi: 10.1080/17460441.2019.1549033.##19. Korkmaz S, Zararsiz G, Goksuluk D. MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development. PLoS One 2015;10(4):e0124600. doi: 10.1371/journal.pone.0124600. ##20. Ahmadi M, Shahlaei M. Quantitative structure-activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods. Res Pharm Sci 2015;10(4):307-25. ##21. Shahlaei M, Bahrami G, Abdolmaleki S, Sadrjavadi K. Majnooni MB. Application of unfolded principal component analysis–radial basis function neural network for determination of celecoxib in human serum by three-dimensional excitation–emission matrix fluorescence spectroscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2015; 138.675-83. doi.org/10.1016/j.saa.2014.12.007##22. Shahlaei M, Madadkar-Sobhani A, Fassihi A, Saghaie L, Arkan E. QSAR study of some CCR5 antagonists as anti-HIV agents using radial basis function neural network and general regression neural network on the basis of principal components. Medicinal Chemistry Research 2012;21(10):3246-62. doi: 10.1007/s00044-011-9863-2 ##

ارزیابی تأثیر عوامل مربوط به ارائه‌دهنده و گیرنده خدمت بر پذیرش خدمات الکترونیک سلامت Assessing the Impact of Health Service Providers and Recipients on the Adoption of E-health Services 1 1 مقدمه: با افزایش سن جمعیت، ملاحظات زمانی، هزینه استفاده حضوری از خدمات مراقبت‌های بهداشتی و درمانی و افزایش مراجعات غیر‌ضروری به مراکز ارئه خدمت، نارضایتی میان ارائه‌دهندگان و نیز گیرندگان خدمات سلامت مشاهده می شود. با ارائه خدمات الکترونیک بخشی از این نارضایتی مرتفع می‌شود. این امر مستلزم پذیرش خدمات الکترونیک سلامت است. با توجه به هزینه سرمایه‌گذاری‌ها در این حوزه برای ارائه‌دهندگان خدمت، رسیدن به نتایج مطلوب بسیار مهم است. با توجه به محدودیت‌های تحقیقات انجام ‌شده از جمله جامع نبودن تحقیقات، تکیه بر خود اظهاری پاسخ‌دهندگان و عدم توجه هم‌زمان به ارائه‌دهنده و گیرنده خدمت، بررسی جامع عواملی که بیشترین تأثیر را بر پذیرش این خدمات داشته و بخشی از محدودیت‌ها را پوشش دهد، بسیار حائز اهمیت است. روش: این تحقیق از نوع توصیفی- کاربردی است. نمونه آماری پژوهش حاضر را خبرگان حوزه فناوری اطلاعات و پزشکان تشکیل دادند. ابزار اندازه‌گیری نیز از نوع کیفی است که با استفاده از مصاحبه و پرسشنامه انجام شد و به منظور گردآوری و تحلیل داده‌ها از روش دلفی فازی استفاده شد. نتایج: مقادیر متغیرهای فازی‌زدایی شده نشان داد که میانگین تمامی شاخص‌ها بیشتر از 0/5 است؛ لذا تمامی شاخص‌ها تأیید ‌شدند. یافته‌های پژوهش نشان داد که ارائه‌دهندگان و گیرندگان خدمات سلامت، بر استفاده از خدمات الکترونیک سلامت مؤثر هستند. نتیجه‌گیری: پیشنهاد می‌شود مدیران و طراحان با در نظر گرفتن نیازها و ترجیحات هر دو طرف اقدام به ارائه خدمات الکترونیک در بخش سلامت نمایند. 2 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 40 51 2019/10/122018/11/212019/10/272019/04/222019/06/30 1398/4/9 2020/02/232019/12/112020/02/232019/11/22019/08/4 1398/5/13 محمد تقی تقوی فرد MohammadTaghi Taghavifard دکتری تخصصی مهندسی صنایع، دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، تهران، ایران ایران dr.taghavifard@gmail.com اکبر یوسف وند Akbar Yousefvand دانشجوی دکتری تخصصی مدیریت فناوری اطلاعات، پردیس تحصیلات تکمیلی، دانشگاه علامه طباطبائی، تهران، ایران ایران ayoosefvand@gmail.com Health Service Provider Health Service Recipient Acceptance of e-Health Services Information Technology ارائه‌دهنده خدمت سلامت گیرنده خدمت سلامت پذیرش خدمات الکترونیک سلامت فناوری اطلاعات 1. Mou, J. and J. Cohen, Trust and risk in consumer acceptance of e-services: A meta-analysis and a test of competing models. 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تأثیر ارائه برنامه‌های آموزشی از طریق پیام‌رسانِ تلگرام بر افزایش آگاهی فراگیران (مطالعه موردی در زمینه پیشگیری از عفونت‌های بیمارستانی در بهورزان شهرستان بم) 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) 1 1 مقدمه: عفونت بیمارستانی یکی از معضلات نظام سلامت هر کشوری می‌باشد و مهم‌ترین راه کنترل آن آموزش صحیح کارکنان این حوزه است. استفاده از شبکه‌های اجتماعی ابزار قدرتمندی در فرآیند آموزش در اختیار آموزش‌دهندگان سلامت قرار می‌دهد. این مطالعه با هدف ارزیابی کیفیت آموزش از طریق پیام‌رسان تلگرام بر ارتقای آگاهی بهورزان نسبت به پیشگیری از عفونت‌های بیمارستانی صورت گرفت. روش: این مطالعه مقطعی، توصیفی- تحلیلی بود که در آن تمامی بهورزان خانه‌های بهداشت شهرستان بم به روش سرشماری شرکت داشتند. آموزش‌ها از طریق نرم‌افزار پیام‌رسان تلگرام به گروه هدف ارسال شد. قبل و بعد از آموزش آگاهی افراد از طریق پرسشنامه سنجیده شد. سپس داده‌ها توسط نرم‌افزارSPSS  نسخه 23 تحلیل شدند. نتایج: میانگین نمره آگاهی شرکت‌کنندگان قبل و بعد از آموزش از مجموع 26 نمره به ترتیب 3/6±9/9 و 3/7±16/6 بود؛ بنابراین اختلاف نمرات قبل و بعد از آموزش معنی‌دار بود. نتیجه‌گیری: استفاده از شبکه‌های اجتماعی و نرم‌افزار پیام‌رسان تلگرام تأثیر مثبتی در افزایش آگاهی بهورزان نسبت به عفونت‌های بیمارستانی دارد. 2 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. 52 59 2019/10/122018/11/212019/10/272019/04/222019/06/302020/04/12 1399/1/24 2020/02/232019/12/112020/02/232019/11/22019/08/42020/04/27 1399/2/8 اکرم غنی زاده Akram Ghanizadeh کارشناسی ارشد آموزش جامعه نگر در نظام سلامت، دانشگاه علوم پزشکی بم، بم، ایران ایران akramghanizadeh@gmail.com محمدرضا رجبعلی پور MohammadReza Rajabalipour کارشناسی ارشد آموزش بهداشت، مربی، گروه بهداشت عمومی، دانشکده بهداشت، دانشگاه علوم پزشکی بم، بم، ایران ایران mmrr1366@yahoo.com رقیه ارشاد سرابی Roghayeh Ershad Sarabi دکترای تخصصی مدیریت اطلاعات سلامت،استادیار، مرکز تحقیقات انفورماتیک پزشکی، پژوهشکده آینده‌پژوهی در سلامت، دانشگاه علوم پزشکی کرمان، کرمان، ایران ایران a.ershadsarabi@gmail.com Hospital Infections Awareness Virtual Learning Telegram Health workers عفونت‌های بیمارستانی آگاهی آموزش مجازی تلگرام بهورزان 1. 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ارائه یک روش خوشه‌بندی گراف-محور جهت شناسایی جمعیت‌های سلولی در داده‌های توالی‌یابی RNA سلول-منفرد A Graph-Based Clustering Approach to Identify Cell Populations in Single-Cell RNA Sequencing Data 1 1 مقدمه: استفاده از فناوری «توالی­ یابی RNA سلول-منفرد» باعث شناخت بهتر ساختارهای سلولی شده و داده‌های با وضوح بسیار بالایی از بیان ژن‌های مختلف هر سلول را در یک زمان واحد ارائه می‌دهد. یکی از زمینه­ های پرکاربرد در این حوزه، خوشه­بندی داده­ ها بر اساس ژن‌های بیان شده است که بعضاً منتج به شناسایی جمعیت­ های سلولی جدید می­گردد. عملکرد روش­ های پیشنهادی عمدتاً به شکل جمعیت­ ها و ابعاد داده­ ها بستگی دارد؛ لذا توسعه یک روش که بتواند فارغ از این موانع به شناسایی جمعیت­ های سلولی بپردازد، بسیار مهم است. روش: در روش پیشنهادی که یک روش کتابخانه­ ای بود، ابتدا تعداد جمعیت‌های سلولی تخمین زده شد. این تخمین از آن جهت اهمیت دارد که در دنیای واقعی، اطلاعات اولیه مثل تعداد و نوع جمعیت‌های سلولی در دسترس نیست. سپس با استفاده از یک کرنل گاوسی مبتنی بر گراف، ضمن کاهش ابعاد مسئله، اقدام به شناسایی جمعیت‌های سلولی با روش خوشه‌بندی kmeans++ شد. نتایج: نتایج پیاده‌سازی نشان داد که روش پیشنهادی می‌تواند نسبت به سایر روش‌های یادگیری ماشین ارائه شده در این زمینه، بهبود قابل قبولی را حاصل کند. به عنوان مثال برای معیار ARI، مقادیر 100، 93/47 و 84/69 به ترتیب برای مجموعه داده‌های سلول-منفرد Kolod، Buettner و Usoskin حاصل شد. نتیجه­ گیری: روش پیشنهادی بدون هیچ اطلاعات اولیه در مورد تعداد و نوع جمعیت‌های سلولی و فارغ از ابعاد بالای مسئله، می­تواند اقدام به خوشه‌بندی و در نتیجه شناسایی جمعیت‌های سلولی با دقت و کیفیت بالایی نماید. 2 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. 60 72 2019/10/122018/11/212019/10/272019/04/222019/06/302020/04/122019/07/16 1398/4/25 2020/02/232019/12/112020/02/232019/11/22019/08/42020/04/272019/12/14 1398/9/23 امین عینی پور Amin Einipour گروه مهندسی کامپیوتر، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران ایران a.einipour@gmail.com محمد مصلح Mohammad Mosleh گروه مهندسی کامپیوتر،واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران ایران mosleh@iaud.ac.ir کریم انصاری اصل Karim Ansari-Asl گروه مهندسی برق، دانشکده مهندسی، دانشگاه شهید چمران اهواز، اهواز، ایران ایران karimansari@scu.ac.ir Single-cell RNA-sequencing Clustering Identification of Cell Populations Graph-based Gaussian Kernel توالی‌یابی RNA سلول-منفرد خوشه‌بندی شناسایی جمعیت‌های سلولی کرنل گاوسی مبتنی بر گراف 1. 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Yanglan G, Ning L, Guobing Z, Yongchang X, Jihong G. Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method. BMC Med Genomics 2018;11(Suppl 6):117. doi: 10.1186/s12920-018-0433-z.##8. Nguyen A, Khoo WH, Moran I, Croucher PI, Phan TG. Single Cell RNA Sequencing of Rare Immune Cell Populations. Front Immunol 2018;9:1553. doi: 10.3389/fimmu.2018.01553.##9. Xiaoqing Yu, Y. Ann Chen, Jose R. Conejo-Garcia, Christine H. Chung, Xuefeng Wang. Estimation of immune cell content in tumor using single-cell RNA-seq reference data. BMC Cancer 2019;19(1):715. doi: 10.1186/s12885-019-5927-3.##10. Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014;343(6172):776-9. doi: 10.1126/science.1247651.##11. Zurauskiene J, Yau C. pcaReduce: Hierarchical Clustering of Single Cell Transcriptional Profiles. BMC Bioinformatics 2016;17:140. doi: 10.1186/s12859-016-0984-y. ##12. Guo M, Wang H, Potter SS, Whitsett JA, Xu Y. Sincera: A pipeline for single-cell RNA-seq profiling analysis. PLoS Comput Biol 2015;11(11):e1004575. doi: 10.1371/journal.pcbi.1004575.##13. Xu C, Su Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 2015;31(12):1974-80. doi: 10.1093/bioinformatics/btv088.##14. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. Introduction to Algorithms. 3rd ed. Cambridge: MIT Press; 2009.##15. Bie R, Mehmood R, Ruan S, Sun Y, Dawood H. Adaptive fuzzy clustering by fast search and find of density peaks. Personal and Ubiquitous Computing 2016;20(5):1-9. doi: 10.1007/s00779-016-0954-4##16. Pouyan MB, Kostka D. Random forest based similarity learning for single cell RNA sequencing data. Bioinformatics 2018;34(13):i79-i88. doi: 10.1093/bioinformatics/bty260.##17. Alpaydin E. Introduction to Machine Learning. 3rd ed. Cambridge: MIT Press; 2015.##18. Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 2014;11(3):333-7. doi: 10.1038/nmeth.2810.##19. Arthur D, Vassilvitskii S. K-means++: The advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms; 2007 Jun; Philadelphia, PA, USA: Society for Industrial and Applied Mathematics; 2007. p. 1027-35. https://dl.acm.org/doi/10.5555/1283383.1283494##20. Kolodziejczyk AA, Kim JK, Tsang JC, Ilicic T, Henriksson J, Natarajan KN, et al. Single cell rna-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 2015;17(4):471-85. doi: 10.1016/j.stem.2015.09.011.##21. Buettner F, Natarajan K, Casale F, Proserpio V, Scialdone A, Theis F, et al. Computational Analysis of Cell-To-Cell Heterogeneity in Single-Cell RNA-sequencing Data Reveals Hidden Subpopulations of Cells. Nat Biotechnol 2015;33(2):155-60. doi: 10.1038/nbt.3102.##22. Usoskin D, Furlan A, Islam S, Abdo H, Lӧnnerberg P, Lou D, et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat Neurosci 2015;18(1):145-53. doi: 10.1038/nn.3881.##1. Wang Z, Gerstein M, Snyder M. RNA-Seq: A Revolutionary Tool for Transcriptomics. Nat Rev Genet 2009;10(1):57-63. doi: 10.1038/nrg2484. ##2. Nagalakshmi U, Waern K, Snyder M. RNA-Seq: A Method for Comprehensive Transcriptome Analysis. Curr Protoc Mol Biol 2010;Chapter 4:Unit 4.11.1-13. doi: 10.1002/0471142727.mb0411s89.##3. Eberwine J, Sul JY, Bartfai T, Kim J. The promise of single-cell sequencing. Nat Methods 2014;11(1):25-7. doi: 10.1038/nmeth.2769.##4. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. mRNA-Seq Whole-Transcriptome Analysis of a Single Cell. Nat Methods 2009;6(5):377-82. doi: 10.1038/nmeth.1315.##5. Poirion OB, Zhu X, Ching T, Garmire L. Single-Cell Transcriptomics Bioinformatics and Computational Challenges. Front Genet 2016;7:163. doi: 10.3389/fgene.2016.00163.##6. Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis. Nat Methods 2014;11(7):740‐2. doi:10.1038/nmeth.2967##7. Yanglan G, Ning L, Guobing Z, Yongchang X, Jihong G. Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method. BMC Med Genomics 2018;11(Suppl 6):117. doi: 10.1186/s12920-018-0433-z.##8. Nguyen A, Khoo WH, Moran I, Croucher PI, Phan TG. Single Cell RNA Sequencing of Rare Immune Cell Populations. Front Immunol 2018;9:1553. doi: 10.3389/fimmu.2018.01553.##9. Xiaoqing Yu, Y. Ann Chen, Jose R. Conejo-Garcia, Christine H. Chung, Xuefeng Wang. Estimation of immune cell content in tumor using single-cell RNA-seq reference data. BMC Cancer 2019;19(1):715. doi: 10.1186/s12885-019-5927-3.##10. Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014;343(6172):776-9. doi: 10.1126/science.1247651.##11. Zurauskiene J, Yau C. pcaReduce: Hierarchical Clustering of Single Cell Transcriptional Profiles. BMC Bioinformatics 2016;17:140. doi: 10.1186/s12859-016-0984-y. ##12. Guo M, Wang H, Potter SS, Whitsett JA, Xu Y. Sincera: A pipeline for single-cell RNA-seq profiling analysis. PLoS Comput Biol 2015;11(11):e1004575. doi: 10.1371/journal.pcbi.1004575.##13. Xu C, Su Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 2015;31(12):1974-80. doi: 10.1093/bioinformatics/btv088.##14. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein. Introduction to Algorithms. 3rd ed. Cambridge: MIT Press; 2009.##15. 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محتوا و ساختار پرونده های الکترونیک سلامت شخصی: یک مرور سیستماتیک The Content and Structure of Electronic Personal Health Records: A Systematic Review 1 1 مقدمه: پرونده الکترونیک سلامت شخصی (ePHR) موجب بهبود آگاهی، مدیریت و ارتقاء سلامت افراد می‌شود. یکی از مهم‌ترین عوامل مؤثر در پیشبرد و توسعه ePHRs، شناسایی، درک محتوا و ساختار آن‌ها می‌باشد. تاکنون مطالعات جامعی در رابطه با محتوا و ساختار آن‌ها صورت نگرفته است؛ بنابراین هدف مطالعه حاضر‌، مروری سیستماتیک بر محتوا و ساختار ePHRs بود. روش: در این مطالعه مروری سیستماتیک، مطالعات منتشر شده انگلیسی زبان با استفاده از کلمات کلیدی در پایگاه‌های اطلاعاتی PubMed، Scopus، Medline Ovid، ScienceDirect و IEEE بین سال‌های 2000 تا 2019 جستجو و بازیابی گردید. سپس، مقالات بر اساس معیار‌های ورود و خروج انتخاب و از جنبه‌های منابع اطلاعاتی سیستم، ماژول‌های طراحی شده، استانداردها و معماری مورد استفاده مورد بررسی قرار گرفتند. نتایج: در نهایت 38 مقاله تحقیقی اصیل انتخاب گردید. نتایج نشان داد که اکثر ePHRs از معماری‌های اتصال‌گرا و یا ترکیبی استفاده نموده‌اند که کاربران از طریق سرور‌های مبتنی بر وب به آن‌ها دسترسی داشتند. همچنین در اکثر ePHRs، از پرونده‌های الکترونیک سلامت و یا پزشکی به عنوان منابع اطلاعاتی از طریق لینک دیتا استفاده شده بود. استانداردهای ترمینولوژی، محتوا، تبادل، امنیتی و حریم خصوصی ePHRs نیز در تعدادی از مطالعات گزارش شده بود. محتوای اکثر ePHRs به دلیل تنوع جامعه هدف شامل شهروندان و بیماران (از جمله بیماران با شرایط مزمن) متنوع بود. نتیجه‌گیری: نتایج این مطالعه می‌تواند در طراحی ePHR‌ها برای اهداف متنوع مورد استفاده محققان و طراحان قرار گیرد. 2 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. 73 90 2019/10/122018/11/212019/10/272019/04/222019/06/302020/04/122019/07/162020/02/8 1398/11/19 2020/02/232019/12/112020/02/232019/11/22019/08/42020/04/272019/12/142020/04/11 1399/1/23 اسماعیل تونی Esmaeel Toni دانشجوی کارشناسی ارشد انفورماتیک پزشکی، گروه فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران toni.esmaeel@gmail.com حبیب الله پیرنژاد Habibollah Pirnejad دکتری تخصصی انفورماتیک پزشکی، دانشیار، مرکز تحقیقات ایمنی بیمار و گروه فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران pirnejad.h@umsu.ac.ir زهرا نیازخانی Zahra Niazkhani دکتری تخصصی انفورماتیک پزشکی، دانشیار، مرکز تحقیقات نفرولوژی و پیوند کلیه و گروه فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران niazkhani.z@umsu.ac.ir Electronic Personal Health Record Systematic Review Content and Structure ePHR پرونده الکترونیک سلامت شخصی مرور سیستماتیک محتوا و ساختار ePHR 1. 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