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مجموعه حداقل داده‌های نرم‌افزار آموزشی خود مراقبتی بیماران مبتلا به سیروز کبدی A Minimum Data Set of Educational Self-care Software for Patients with Cirrhosis 1 1 مقدمه: سیروز کبدی در چند سال اخیر در کشورهای آسیایی رشد قابل‌توجهی داشته است. بـا ایـن وجـود، ابـزاری بـرای گـردآوری داده‌هـای سیروز کبدی ایجـاد نشـده اسـت. هـدف ایـن مطالعـه طراحـی حداقـل مجموعـه داده بـرای بیماری سیروز کبدی اسـت. روش: این مطالعه مقطعی در زمستان 1396 با همکاری ۱۶ نفر از متخصصین کبد بیمارستان‌های حضرت رسول (ص) و فیروزگر تهران انجام گردید. ابزار پژوهش پرسشنامه‌ای محقق ساخته بود. به منظور روایی پرسشنامه از روش اعتبار محتوا و به منظور پایایی آن از روش آزمون- بازآزمون استفاده شد (0.84=r). تحلیل داده‌ها با استفاده از نرم افزار SPSS نسخه 21 انجام گردید. نتایج: حداقــل مجموعــه داده در پنج دسته داده دموگرافیک، بالینی، علائم بیماری، توصیه‌های غذایی، توصیه‌های عمومی، داروها و یادآورها و سازماندهی شـد. داده‌های دموگرافیک شامل 15 عنصر، بالینـی ۲۷، علائم بالینی ۹، توصیه‌های غذایی ۷ بخش و ۱۴ عنصر، توصیه‌های عمومی متشکل از ۷ بخش و ۱۲ عنصر و داروها و یادآورها نیز متشکل از ۲ بخش و ۱۲ عنصر بود. نتیجه­ گیری: با توجه به اهمیت داده و اطلاعات در خودمراقبتی بیماران سیروز کبدی و نقشی که نرم افزار‌ها می‌توانند در جمع‌آوری و به اشتراک‌گذاری داده با پزشک و همچنین کاهش مراجعات به بیمارستان داشته باشند، توجه به حداقل داده بیماران مبتلا به سیروز کبدی ضروری می‌باشد. 2 Introduction: Liver cirrhosis has grown significantly in Asian countries over the last few years. However, there is no tool for collecting liver cirrhosis data. The purpose of this study was to design a minimum data set for the self-care education of liver cirrhosis. Methods: This descriptive study was conducted in the first half of 2018 in cooperation with 16 liver specialists of Rasool Akram Hospital and Firoozgar Hospital in Tehran. The research tool was a researcher-made questionnaire. The content validity method was used to validate the questionnaire and its reliability was confirmed through test-retest method (r = 0.84). Data were analyzed using SPSS21 software. Results: The minimum data set was organized in six categories: demographic, clinical, disease symptoms, dietary recommendations, general recommendations, medications and reminders. The demographic data collection consisted of 15 data elements, clinical data included 27 data elements, clinical symptoms included 9 data elements, food recommendations, consisted of 7 parts and 14 data elements, general recommendations consisted of 7 parts and 12 elements of data, and medications and reminders had two parts and 12 elements. Conclusion: Considering the importance of data and information in the self-care of patients with liver cirrhosis and the role of software in collecting and sharing data with physicians, as well as reducing the number of visits to the hospital, attention should be paid to the minimum data set of self-care in patients with liver cirrhosis. 264 271 2018/05/31 1397/3/10 2019/11/12 1398/8/21 هاله فرساد حبیبی Haleh Farsadhabibi کارشناسی ارشد فناوری اطلاعات سلامت، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران h.Farsadhabibi@yahoo.com مصطفی لنگری زاده Mostafa Langarizadeh دکترای 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 مصطفی علوی مقدم Mostafa Alavi Moghadam متخصص گوارش و کبد استاد، دانشکده پزشکی، دانشگاه علوم پزشکی شهید بهشتی، تهران، ایران ایران mosalavi@yahoo.com Data Minimum Set Self-care Cirrhosis مجموعه حداقل داده خودمراقبتی سیروز کبدی 1. Koupai A, Moheb B, Vazifeh F. Organ transplants. Tehran: Foundation of Certain Diseases; 2013.##2. Wedro B. Liver Disease (Hepatic Disease) 2012 [cited 2018 Jan 10] Available from: www.medicinenet.com/liver_disease/article.htm.##3. Nematollahi M, Shacheraghi F, Sharifian R. The Comparative Study of Liver Diseases Surveillance System in Selected Countries. Health Inf Manage 2014;11(4):411-5. [In Persian]##4. Jafari N, Farajzadegan Z, Ataei B. Surveillance system for hepatitis C infection: A practical approach. Int J Prev Med 2012;3(Suppl 1):S48-57. ##5. Zandi M, Adib-Hajbagheri M, Memarian R, Nejhad AK, Alavian SM. Effects of a self-care program on quality of life of cirrhotic patients referring to Tehran Hepatitis Center. Health Qual Life Outcomes 2005;3:35. doi: 10.1186/1477-7525-3-35##6. Volk ML, Fisher N, Fontana RJ. Patient knowledge about disease self-management in cirrhosis. Am J Gastroenterol 2013;108(3):302-5. doi: 10.1038/ajg.2012.214.##7. Lau-Walker M, Presky J, Webzell I, Murrells T, Heaton N. Patients with alcohol-related liver disease--beliefs about their illness and factors that influence their self-management. J Adv Nurs 2016;72(1):173-85. doi: 10.1111/jan.12826. ##8. Karen M. nutritional help guide concerns in rheumatoid arthritis. Arthritis Rheumatol. 2017;7(1):3-11.##9. Sakakibara BM, Ross E, Arthur G, Brown-Ganzert L, Petrin S, Sedlak T, et al. Using Mobile-Health to Connect Women with Cardiovascular Disease and Improve Self-Management. Telemed J E Health 2017;23(3):233-9. doi: 10.1089/tmj.2016.0133.##10. Malekzadeh J, Amouzeshi Z, Mazloum R. The Effect of Teaching Orem Self- Care Model on Nursing Students Communication Performance in Clinical Setting. Modern Care Journal 2011; 8(3):107 -15. [In Persian]##11. Beste LA, Harp BK, Blais RK, Evans GA, Zickmund SL. Primary Care Providers Report Challenges to Cirrhosis Management and Specialty Care Coordination. Dig Dis Sci 2015;60(9):2628-35. doi: 10.1007/s10620-015-3592-1.##12. Reese JB, Somers TJ, Keefe FJ, Mosley-Williams A, Lumley MA. Pain and functioning of rheumatoid arthritis patients based on marital status: is a distressed marriage preferable to no marriage? J Pain 2010;11(10):958-64. doi: 10.1016/j.jpain.2010.01.003.##13. Sheikhian MR, Meysami Bonab S, Ahadi M, Maasoomi E. Relationship between Behavioral Disorders and Quality of Life in Patients with Liver Cirrhosis. Govaresh 2014;19:20-5.##14. Nematzadeh M. Attitude of the medical evidence about the effectiveness of health information technology system [dissertation]. Tehran University of Medical Sciences; 2005.##15. Schiff E, Sorrell M. Schiff's Diseases of the Liver. 11th ed. Maddrey W, editor. Philadelphia: Lippincott Williams andWilkins; 2011.##16. Langarizadeh M, Gholinezhad M. A Minimum Data Set of Laboratory Reporting System for Exchanging With Electronic Health Record System in Iran. Journal of Health Adminisrtation. 2017;19(66):61-70.[In Persian]##17. Adel T. Proposing a conceptual model of personal health record for autistic patient [Thesis MSc]. Iran university of medical sciences; 2017. [In Persian]##18. Samadpor H, Sadoghi F. [Data Set of electronic Personal Health Records for Iranian Students of Medical Sciences]. Journal of Health Information Management. 2014; 12(4):416-425. [In Persian]##19. Ahmadi M, Alipour J, Mohammadi A, Khorami F. Development a minimum data set of the information management system for burns. Burns : journal of the International Society for Burn Injuries. 2015;41(5):1092-9.##20. Davey CJ, Slade SV, Shickle D. A proposed minimum data set for international primary care optometry: a modified Delphi study. Ophthalmic & Physiological Optics: the journal of the British College of Ophthalmic Opticians (Optometrists). 2017;37(4):428-39.##1. Koupai A, Moheb B, Vazifeh F. Organ transplants. Tehran: Foundation of Certain Diseases; 2013.##2. Wedro B. Liver Disease (Hepatic Disease) 2012 [cited 2018 Jan 10] Available from: www.medicinenet.com/liver_disease/article.htm.##3. Nematollahi M, Shacheraghi F, Sharifian R. The Comparative Study of Liver Diseases Surveillance System in Selected Countries. Health Inf Manage 2014;11(4):411-5. [In Persian]##4. Jafari N, Farajzadegan Z, Ataei B. Surveillance system for hepatitis C infection: A practical approach. Int J Prev Med 2012;3(Suppl 1):S48-57. ##5. Zandi M, Adib-Hajbagheri M, Memarian R, Nejhad AK, Alavian SM. Effects of a self-care program on quality of life of cirrhotic patients referring to Tehran Hepatitis Center. Health Qual Life Outcomes 2005;3:35. doi: 10.1186/1477-7525-3-35##6. Volk ML, Fisher N, Fontana RJ. Patient knowledge about disease self-management in cirrhosis. Am J Gastroenterol 2013;108(3):302-5. doi: 10.1038/ajg.2012.214.##7. Lau-Walker M, Presky J, Webzell I, Murrells T, Heaton N. Patients with alcohol-related liver disease--beliefs about their illness and factors that influence their self-management. J Adv Nurs 2016;72(1):173-85. doi: 10.1111/jan.12826. ##8. Karen M. nutritional help guide concerns in rheumatoid arthritis. Arthritis Rheumatol. 2017;7(1):3-11.##9. Sakakibara BM, Ross E, Arthur G, Brown-Ganzert L, Petrin S, Sedlak T, et al. Using Mobile-Health to Connect Women with Cardiovascular Disease and Improve Self-Management. Telemed J E Health 2017;23(3):233-9. doi: 10.1089/tmj.2016.0133.##10. Malekzadeh J, Amouzeshi Z, Mazloum R. The Effect of Teaching Orem Self- Care Model on Nursing Students Communication Performance in Clinical Setting. Modern Care Journal 2011; 8(3):107 -15. [In Persian]##11. Beste LA, Harp BK, Blais RK, Evans GA, Zickmund SL. Primary Care Providers Report Challenges to Cirrhosis Management and Specialty Care Coordination. Dig Dis Sci 2015;60(9):2628-35. doi: 10.1007/s10620-015-3592-1.##12. Reese JB, Somers TJ, Keefe FJ, Mosley-Williams A, Lumley MA. Pain and functioning of rheumatoid arthritis patients based on marital status: is a distressed marriage preferable to no marriage? J Pain 2010;11(10):958-64. doi: 10.1016/j.jpain.2010.01.003.##13. Sheikhian MR, Meysami Bonab S, Ahadi M, Maasoomi E. Relationship between Behavioral Disorders and Quality of Life in Patients with Liver Cirrhosis. Govaresh 2014;19:20-5.##14. Nematzadeh M. Attitude of the medical evidence about the effectiveness of health information technology system [dissertation]. Tehran University of Medical Sciences; 2005.##15. Schiff E, Sorrell M. Schiff's Diseases of the Liver. 11th ed. Maddrey W, editor. Philadelphia: Lippincott Williams andWilkins; 2011.##16. Langarizadeh M, Gholinezhad M. A Minimum Data Set of Laboratory Reporting System for Exchanging With Electronic Health Record System in Iran. Journal of Health Adminisrtation. 2017;19(66):61-70.[In Persian]##17. Adel T. Proposing a conceptual model of personal health record for autistic patient [Thesis MSc]. Iran university of medical sciences; 2017. [In Persian]##18. Samadpor H, Sadoghi F. [Data Set of electronic Personal Health Records for Iranian Students of Medical Sciences]. Journal of Health Information Management. 2014; 12(4):416-425. [In Persian]##19. Ahmadi M, Alipour J, Mohammadi A, Khorami F. Development a minimum data set of the information management system for burns. Burns : journal of the International Society for Burn Injuries. 2015;41(5):1092-9.##20. Davey CJ, Slade SV, Shickle D. A proposed minimum data set for international primary care optometry: a modified Delphi study. Ophthalmic & Physiological Optics: the journal of the British College of Ophthalmic Opticians (Optometrists). 2017;37(4):428-39. ##

مدل‌سازی مبتنی بر عامل برای شبیه‌سازی بافت پستان و رشد و گسترش تومور در وضعیت‌های مختلف سرطان پستان An Agent- based Modeling for Breast Tissue Simulation and the Growth and Spread of Tumor in Various Breast Cancer States 1 1 مقدمه: سرطان پستان سرطانی است که از رشد غیرعادی سلول­­های پستان ایجاد می­شود. مدل­سازی و شبیه­سازی رشد و درمان سرطان پستان می­تواند ضمن فراهم آوردن امکان انجام آزمایش­ها و تحقیقات، زمان و هزینه­های درمان را از طریق پیش­بینی برخی موارد کاهش دهد. هدف از پژوهش ایجاد مدلی مبتنی بر عامل برای شبیه­سازی نمودن بافت پستان و رشد و گسترش تومور در وضعیت‌های مختلف سرطان پستان بود. روش: در این پژوهش از روش مدل‌سازی و شبیه‌سازی مبتنی بر عامل و مطالعات کتابخانه‌ای استفاده شد. با استفاده از نرم‌افزار NetLogo اقدام به طراحی و مدل‌سازی آناتومی پستان و توسعه مدل با دانش مورد نیاز شد. سپس با توجه به سن بیمار و درجه‌بندی تومور و چگونگی گسترش سلول‌های سرطانی، شبیه‌سازی رشد و تکامل وضعیت‌های مختلف سرطان پستان در زمان‌های مختلف مورد بررسی قرار گرفت. برای اعتبار‌سنجی مدل، اطلاعات و گزارش‌های بالینی مربوط به سن، درجه، سایز و رشد و گسترش تومور تعدادی از بیماران مبتلا به سرطان پستان مورد مطالعه و مدنظر قرار گرفت و با خروجی‌های مدل مقایسه شد. نتایج: آزمایش‌های مربوط به تحلیل و اعتبار‌سنجی مدل نشان داد که مدل توسعه یافته، توانست نتایج نسبتاً قابل قبولی در خصوص رشد تومور پستان و تکثیر سلول‌های سرطانی در غدد لنفاوی در وضعیت‌های مختلف سرطان پستان به همراه داشته باشد.  نتیجه­ گیری: مدل‌سازی مبتنی بر عامل می‌تواند به طور نسبتاً مفید و کارا در شناسایی و تحلیل رفتار سرطان پستان و پیش‌بینی رشد و تکثیر سلول‌های سرطانی مورد استفاده قرار گیرد. 2 Introduction: Breast cancer is a cancer that is caused by abnormal growth of breast cells. Modeling  and simulation of the growth and treatment of breast cancer, along with providing the possibility of doing experiments and research, can reduce the time and cost of treatment by predicting some cases. The purpose of the present research was to develop an agent-based model for the simulation of breast tissue and the growth and spread of tumor in various breast cancer states. Method: In this research, agent-based modeling and simulation method, as well as library studies were used. NetLogo software was used for designing and modeling of breast anatomy and development of model with the required knowledge. Then, according to the patient's age, tumor grading and cancer cells spread, simulation of the growth and development of various breast cancer states in different times were investigated. In order to validate the model, clinical data and reports related to the age and grade, size, growth and spread of tumor in a number of patients with breast cancer were studied and considered and compared with model outcomes. Results: According to the experiments related to the analysis and validation of the model, the developed model had relatively acceptable results about the growth of breast tumors and the proliferation of cancerous cells in lymph nodes in various breast cancer states.  Conclusion: Agent-based modeling can be used as a relatively useful and efficient method in identifying and analyzing breast cancer behavior and predicting the growth and proliferation of cancer cells. 272 287 2018/05/312019/06/30 1398/4/9 2019/11/122019/08/25 1398/6/3 سهیلا مردانی Soheila Mardani کارشناسی ارشد مهندسی کامپیوتر، گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، واحد رودهن، دانشگاه آزاد اسلامی، رودهن، ایران ایران mardani681@yahoo.com آرش رحمان Arash Rahman دکترای مهندسی کامپیوتر، استادیار، گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، واحد رودهن، دانشگاه آزاد اسلامی، رودهن، ایران ایران arahman@riau.ac.ir ناهید نفیسی Nahid Nafissi فلوشیپ جراحی پستان، استادیار، گروه آموزشی جراحی، دانشکده پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران Nafissi.n@iums.ac.ir Agent-based modeling Breast cancer Simulation of breast cancer مدلسازی مبتنی بر عامل سرطان پستان شبیه‌سازی سرطان پستان 1. Kaviani A, Ebrahimi M, Majidzade K, Najafi M, Haghighat S. A trip with breast cancer towards hope. 1st ed. Qom: Ebtekare Danesh; 2010. [In Persian]##2. Mehrian M. Modeling Immunotherapy Methods and growth of cancer cells using statistical methods and artificial intelligence [dissertation]. Tehran: Khajeh Nasir Toosi University of Technology; 2012. [In Persian]##3. Khani H, Mirzaei K. Modeling and simulating cancer behavior in the framework of Multi agents systems based on complex networks theory. 8th Symposium on Advances in Science & Technology- Computer Networks, Modeling & System Security (CNMSecur); 2013 Dec 28; Mashhad: Khavaran Institute of Higher Education; 2013. [In Persian]##4. Pourhasanzade F, Sabzpoushan SH, Alizade AM, Esmati E. An agent-based model of avascular tumor growth: Immune response tendency to prevent cancer development. Simulation 2017; 93(8): 641-57. /doi.org/10.1177/0037549717699072##5. Ghaffari A, Azizi K, Amini MR. 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ارزیابی اکتشافی سیستم آرشیو و انتقال تصاویر رادیولوژی (پکس) Heuristic Evaluation of Picture Archiving and Communication Systems (PACS) 1 1 مقدمه: رابط کاربری ضعیف می‌تواند به‌ عنوان منبع احتمالی بروز خطا و کاهش انگیزه کاربران در استفاده از سیستم‌های اطلاعات سلامت مانند سیستم پکس باشد. ارزیابی اکتشافی یکی از روش‌های اطمینان از مناسب بودن رابط کاربری در سیستم‌های اطلاعات سلامت مانند پکس است. روش: این مطالعه از نوع توصیفی-تحلیلی بود که به ‌صورت مقطعی در سال 1397 با استفاده از مجموعه اصول سیزده‌گانه نلسون انجام شد. محیط پژوهش در این مطالعه دانشگاه علوم پزشکی زنجان بود و تحلیل داده‌ها با استفاده از روش‌های آمار توصیفی و تحلیلی در محیط نرم‌افزار SPSS نسخه 25 انجام گردید. نتایج: در این مطالعه مجموعاً 130 مشکل از 293 مورد کاربردپذیری یافت شد. بیشترین مشکلات مربوط به کاربردپذیری به ترتیب شامل حریم خصوصی (66/66 درصد)، شفافیت وضعیت سیستم (65/52 درصد) و انعطاف‌پذیری و کارایی استفاده (60 درصد) و کمترین تعداد خطا یا مشکل کاربردپذیری مربوط به مؤلفه تعامل و احترام به کاربر (14/29 درصد) بود. نتیجه‌گیری: ارزیابی اکتشافی می‌تواند ریشه و عامل مشکلاتی را شناسایی نماید که گاهی باعث ایجاد خطای پزشکی یا مقاومت کاربران در استفاده از سیستم‌های اطلاعات سلامت شده است؛ بنابراین لازم است توسعه‌دهندگان سیستم‌های بالینی مانند پکس که محصولات آن‌ها در سطح وسیع و در مراکز مهم درمانی مورد استفاده قرار می‌گیرند، استانداردهای رابط کاربری را در طراحی این سیستم‌ها رعایت نموده و از بروز هرگونه خطا پیشگیری نمایند. 2 Introduction: Poor User Interface design can be one of the probable reasons of users’ error and reduced incentive in using health information systems such as PACS. Heuristic evaluation is one the methods for assuring the proper user interface design of health information systems such as PACS. Method: This study was a descriptive-analytic research conducted in 2019 through using Nielsen usability checklist. The study setting was Zanjan University of Medical Sciences and data were analyzed in SPSS 25 using descriptive statistics. Results: In whole, 130 problems were found in 293 usability cases. The most common usability problems include privacy (66.66%), Visibility of System Status (65.52%), and Flexibility and Minimalist Design (60%) and the lowest number of UI design problems were associated with the Pleasurable and Respectful Interaction with the User (14.29%). Conclusion: Usability Evaluation can identify the reasons of problems that may cause medical errors or users' resistance in working with the system. Therefore, it is necessary for system developers whose systems are widely used in important centers to pay more attention to all standards in designing the system and to prevent the occurrence of errors.   288 297 2018/05/312019/06/302019/07/9 1398/4/18 2019/11/122019/08/252019/08/29 1398/6/7 محسن سلیمانی Mohsen Soleimani کارشناسی ارشد انفورماتیک پزشکی، مرکز آموزشی و درمانی آیتاله موسوی، دانشگاه علوم پزشکی زنجان، زنجان، ایران ایران mohsensoleymani66@gmail.com علی نصیری Ali Nasiri کارشناسی ارشد مهندسی نرم‌افزار، مرکز آموزشی و درمانی آیتاله موسوی، دانشگاه علوم پزشکی زنجان، زنجان، ایران ایران nasirimail@gmail.com Usability evaluation Heuristic evaluation Picture archiving and communication system (PACS) Radiology Information System ارزیابی کاربردپذیری ارزیابی اکتشافی سیستم آرشیو و انتقال تصاویر پکس سیستم اطلاعات رادیولوژی 1. Reed ME. The health information technology special issue: new real-world evidence and practical lessons. Am J Manag Care 2019;25(1):12.##2. 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تشخیص بیماری بروسلوزیس در شهرستان رفسنجان با استفاده از شبکه های عصبی مصنوعی آتوانکو در عمیق The Diagnosis of Brucellosis in Rafsanjan City Using Deep Auto-Encoder Neural Networks 1 1 مقدمه: بیماری تب مالت (بروسلوزیس) یکی از مهم‌ترین بیماری‌های عفونیِ مشترک بین انسان و دام محسوب می‌شود. با توجه به اندمیک بودن تب مالت و وجود گزارش‌های متعددی از موارد انسانی و حیوانی آن، میزان شیوع تب مالت انسانی در شهرستان رفسنجان در طول 3 سال (97-1395) تعیین گردید. هدف از این مطالعه یافتن یک تطابق و همسان‌سازی روشی خودکار و هوشمندانه با حساسیت کم بر مبنای شبکه عصبی است، که قادر به تشخیص دقیق تب مالت ‌باشد. روش: در این مطالعه توصیفی-تحلیلی موارد بروسلوز انسانی بر اساس شاخص‌های جنس، سن، بارداری، سابقه تماس با دام و استفاده از مواد غیر‌پاستوریزه لبنی، پارامترهای آزمایشگاهی Wright و 2ME در طول 3 سال از شهرستان رفسنجان واقع در جنوب ایران تحلیل شد. داده‌ها به دو زیر مجموعه آموزش (80%) و آزمون (20%) تقسیم شدند. از روش شبکه عصبی مصنوعی آتوانکو در عمیق برای آموزش هر زیر مجموعه استفاده شد. نتایج: روش آتوانکو در عمیق به صحت 90/84% حساسیت 94/61% و ویژگی 50‌% در پیش‌بینی موارد بیماری بروسلوزیس در مجموعه داده‌های آزمون دست یافت، نتایج تجربی، عملکرد دقیق شبکه عصبی را در تشخیص بیماری بروسلوز نشان داد. نتیجه‌گیری: مدل شبکه عصبی مصنوعی عمیق می‌تواند به عنوان یک روش کارآمد و هوشمند برای تشخیص موارد بروسلوز انسانی به کار گرفته شود. با این حال مطالعه و پژوهش‌های بیشتری برای طراحی مدل‌های دیگر از شبکه‌های عصبی مصنوعی بر اساس یادگیری عمیق جهت تشخیص سایر بیمارهای عفونی مورد نیاز است. 2 Introduction: Brucellosis is considered as one of the most important common infectious diseases between humans and animals. Considering the endemic nature of brucellosis and the existence of numerous reports of human and animal cases of brucellosis in Iran, the incidence of human brucellosis in Rafsanjan city was determined in the last 3 years (2016–2018). The main objective of this study was to find an automated consistent and intelligent method with low sensitivity based on a neural network which is capable of accurate detection of brucellosis disease. Methods: In this descriptive analytic study, cases of human brucellosis in Rafsanjan, south of Iran, were analyzed based on sex, age, pregnancy, history of contact with livestock and the use of non-pasteurized dairy products, Right Laboratory parameters and 2ME during 3 years (2016–2018). Data were split into two subsets of train (80%) and test (20%). The artificial neural network approach of the deep auto-encoder was used to train each subset. Results: The deep auto-encoder method achieves 94.61% sensitivity, 90.84% accuracy and 50% specificity in the diagnosis of brucellosis over the experimental data sets. The experimental results also showed the excellent performance of the proposed artificial neural network. Conclusion: The deep artificial neural network model can be used as an efficient and intelligent method to detect the human cases of brucellosis. However, further studies are required to design other models of artificial neural networks based on deep learning to detect other infectious diseases. 298 308 2018/05/312019/06/302019/07/92018/12/16 1397/9/25 2019/11/122019/08/252019/08/292019/06/1 1398/3/11 حسین قیومی زاده Hossein Ghayoumi Zadeh دکتری مهندسی پزشکی، استادیار، گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه ولی‌عصر(عج) رفسنجان، رفسنجان، ایران ایران h.ghayoumizadeh@vru.ac.ir علی فیاضی Ali Fayazi دکتری مهندسی کنترل، استادیار، گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه ولی‌عصر(عج) رفسنجان، رفسنجان، ایران ایران a.fayazi@vru.ac.ir مصطفی دانائیان Mostafa Danaeian دکتری مهندسی مخابرات، استادیار، گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه ولی‌عصر (عج) رفسنجان، رفسنجان، ایران ایران danaeian@vru.ac.ir آلاء سعیدی Alae Saeidi کارشناس ارشد حشره‌شناسی پزشکی مبارزه با ناقلین، گروه مبارزه با بیماری‌ها، معاونت بهداشتی، دانشگاه علوم پزشکی رفسنجان، رفسنجان، ایران ایران ala.saeidi@gmail.com Human Brucellosis Rafsanjan Artificial neural network Deep auto-encoder method بروسلوز انسانی رفسنجان شبکه عصبی مصنوعی روش خودرمزنگار عمیق تشخیص 1. Shirzadi M, Guideline for the Diagnosis and Treatment of Brucellosis. Tehran: Andishmand; 2013. [In Persian]##2. Abdussalam M, Fein D. Brucellosis as a world problem. Developments in Biological Standardization 1976; 31: 9-23.##3. Taheri Soodejani M, Lotfi M, Ghaderi A, Reisi A, Mohannadzadeh M. Epidemiology of brucellosis in Shahr-e- Kord during the years 2010 to 2014. Pars of Jahrom University of Medical Sciences 2016;14(1):1-7. [In Persian] doi: 10.29252/jmj.14.1.1##4. Heidari M, Nazer M, Kheirollahi A, Birjandi M, Zareie H. Frequency of epididymo-orchitis in hospitalized patients with acute scrotum at Shohadaye Ashayer Hospital, Khorramabad, Iran. J Pak Med Assoc 2012;62(1):44-6.##5. Nabavi M, Hatami H, Jamali Arand H. Epidemiological, Clinical and Paraclinical Surveys of Children and Adolescents with Brucellosis in Kohgiluyeh and Boyer Ahmad Province in Southwest of Iran during 2009-2015. Armaghane Danesh 2018; 23(2):236-52. [In Persian]##6. Mohammadian M, Salehiniya H, Kazaei S, Ramazanpour J, Mohammadian-Hafshejani A. Epidemiological characteristics and incidence rate of brucellosis in Isfahan province. Journal of Isfahan Medical School 2015; 33(355): 1784-95. [In Persian]##7. Pakzad R, Barati M, Moludi J, Barati H, Pakzad I. Epidemiology of Brucellosis in the North and North-West Iran. Paramedical Sciences and Military Health 2016; 11(1):17-23. [In Persian]##8. Hashtarkhani S, Akbari M, Jarahi L, Etminani K. Epidemiological characteristics and trend of incidence of human brucellosis in Razavi Khorasan province. Medical Journal of Mashhad University of Medical Sciences 2015; 58(9): 531-8. [In Persian]##9. Hajari A, Shams M, Afrooghi S, Fadaei Nobari R, Abaspoor Najafabadi R. Brucellosis in Tiran & Karvan: Study of the Disease in the Recent 10-Year Period (2004-2013). Military Caring Sciences 2016; 3(2):100-6. [In Persian] doi: 10.18869/acadpub.mcs.3.2.100##10. Loey M, El-Sawy A, EL-Bakry H. Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition. arXiv 2017.## ##

ارائه یک الگوریتم برای پیش بینی عود بیماران مبتلا به سرطان پستان با استفاده از الگوریتم ژنتیک و الگوریتم نزدیک ترین همسایگی An Algorithm for Predicting Recurrence of Breast Cancer Using Genetic Algorithm and Nearest Neighbor Algorithm 1 1 مقدمه: بیماری سرطان پستان یکی از شایع‌ترین انواع سرطان و شایع‌ترین نوع بدخیمی در زنان است که در سال‌های اخیر روند رو به رشدی داشته است. در مبتلایان به این بیماری همواره احتمال عود مجدد وجود دارد. عوامل زیادی میزان این احتمال را کاهش یا افزایش می‌دهند. داده‌کاوی از روش‌هایی است که در تشخیص یا پیش‌بینی سرطان‌ها به کار می‌رود و یکی از بیشترین کاربردهای آن، پیش‌بینی عود مجدد سرطان پستان است. روش: در این مطالعه گذشته­نگر از داده­های 699 بیمار مبتلا به سرطان پستان با 14 ویژگی استفاده ­شد که از این تعداد 458 نفر (66 درصد) سرطان آن‌ها عود نکرد و 241 نفر (34 درصد) سرطان آن­ها عود کرده است. این اطلاعات از سال 1391 تا 1394 از پرونده بیماران سرطان پستان جهاد دانشگاهی جمع­آوری شد. در این پژوهش از ترکیب دو الگوریتم نزدیک­ترین همسایگی و الگوریتم ژنتیک برای پیش­بینی عود بیماران مبتلا به سرطان پستان استفاده گردید. ابتدا الگوریتم نزدیک‌ترین همسایگی برای پیش­بینی عود سرطان پستان ارائه شد سپس به کمک الگوریتم ژنتیک متغیرهای وابسته کاهش یافت تا مدل صحت مناسب­تری داشته باشد. نتایج: تعداد متغیرهای وابسته 14 متغیر بود که به کمک الگوریتم ژنتیک به 6 متغیر کاهش پیدا نمود تا مدل پیش‌بینی کارایی بهتری داشته باشد. جهت ارزیابی مدل از پارامتر صحت استفاده شد که مقدار آن برای مدل پیشنهادی 14/77 درصد است که نسبت به روش‌های دیگر خروجی مناسب‌تری دارد. نتیجه­ گیری: در این مطالعه الگوریتم پیشنهادی با روش‌های دیگر پیش‌بینی مورد بررسی قرار گرفت و مشخص گردید الگوریتم پیشنهادی دارای صحت بهتر است.   2 Introduction: Breast cancer is one of the most common types of cancer and the most common type of malignancy in women, which has been growing in recent years. Patients with this disease have a chance of recurrence. Many factors reduce or increase this probability. Data mining is one of the methods used to detect or anticipate cancers, and one of its most common uses is to predict the recurrence of breast cancer. Cases and Methods: Out of 699 patients with breast cancer, 458 (66%) of them did not relapse and 241 (34%) of their cancer recurred. This information was collected from 91 to 94 years of history of patients with breast cancer in the academic Jihad. In this study, the combination of two nearest neighboring algorithms and a genetic algorithm are proposed to predict the relapse of patients with breast cancer. First, the nearest neighboring algorithm is presented to predict the recurrence of breast cancer. Then, using the genetic algorithm, the dependent variables are reduced to make the model more appropriate. Results: The number of dependent variables is 14 variables, which is reduced by 6 genetic algorithms to better predict the model. To evaluate the model, the health parameter is used, which is 77.14% for the proposed model, which could not be more suitable for other methods. Conclusion: In this study, the proposed algorithm was examined with other predictive methods and it was determined that the proposed algorithm is better. 309 319 2018/05/312019/06/302019/07/92018/12/162018/12/22 1397/10/1 2019/11/122019/08/252019/08/292019/06/12019/06/17 1398/3/27 ستایش صادقی Setayesh Sadeghi کارشناسی ارشد‌،‌ مهندسی کامپیوتر، گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد کرمان، کرمان، ‌ایران ایران angel.secret2020@gmail.com امین گلاب پور Amin Golabpour دکتری تخصصی انفورماتیک پزشکی، استادیار، دانشگاه علوم پزشکی شاهرود، دانشکده پیراپزشکی، شاهرود، ایران ایران a.golabpour@shmu.ac.ir Breast Cancer Recurrence Genetic Algorithm Nearest Neighbor Algorithm عود سرطان پستان الگوریتم ژنتیک الگوریتم نزدیک‌ترین همسایگی 1. Cameron D, Piccart-Gebhart MJ, Gelber RD, Procter M, Goldhirsch A, de Azambuja E, et al. 11 years' follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive early breast cancer: final analysis of the HERceptin Adjuvant (HERA) trial. Lancet 2017;389(10075):1195-205. doi: 10.1016/S0140-6736(16)32616-2.##2. Denkert C, Liedtke C, Tutt A, von Minckwitz GJTL. Molecular alterations in triple-negative breast cancer-the road to new treatment strategies. Lancet 2017;389(10087):2430-42. doi: 10.1016/S0140-6736(16)32454-0. ##3. DeSantis CE, Ma J, Goding Sauer A, Newman LA, Jemal A. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin 2017;67(6):439-48. doi: 10.3322/caac.21412. ##4. Dehghan P, Mogharabi M, Zabbah I, Layeghi K, Maroosi A. Modeling Breast Cancer Using Data Mining Methods. Journal of Health and Biomedical Informatics 2018; 4(4):266-78. [In Persian]##5. Kiani B, Atashi A. A Prognostic Model based on data mining techniques to predict breast cancer recurrence. Journal of Health and Biomedical Informatics 2014; 1(1):26-31.[In Persian]##6. Ojha U, Goel S. A study on prediction of breast cancer recurrence using data mining techniques. 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence; 2017 Jan 12-13; Noida, India: IEEE; 2017. doi: 10.1109/CONFLUENCE.2017.7943207##7. Silva J, Lezama OBP, Varela N, Borrero LA. Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence. International Conference on Green, Pervasive, and Cloud Computing; 2019; Cham: Springer International Publishing; 2019. p. 18-30.##8. Ghorbani N, Yazdani Cherati J, Anvari K, Ghorbani N. Factors Affecting Recurrence in Breast Cancer Using Cox Model. J Mazandaran Univ Med Sci 2015; 25(131):32-9.[In Persian]##9. Guyton AC, Hall JE. Textbook of Medical Physiology. 13th ed. Philadelphia: Saunders; 2015.##10. Wikipedia. Breast cancer: 2018 [cited 2018 Dec 18]. Available from: https://en.wikipedia.org/w/index.php?title=Breast_cancer&oldid=874275465.##11. Winchester DJ, Winchester DP, Hudis CA, Norton L. Breast Cancer. 2nd ed. Ontario, Canada: B.C. Decker Inc; 2006.##12. Sivanandam SN, Deepa SN. Introduction to Genetic Algorithms: Springer Berlin Heidelberg; 2007.##13. Man KF, Tang KS, Kwong S. Genetic Algorithms: Concepts and Designs. London: Springer; 2012.##14. Beasley D, Bull DR, Martin RR. An overview of genetic algorithms: Part 1, fundamentals. University Computing 1993;15(2):1-16.##15. Witten IH, Frank E, Hall MA. Data Mining: Practical Machine Learning Tools and Techniques. 4th ed. USA: Morgan Kaufmann; 2011.## ##

طراحی، مدل‌سازی و آنالیز محاسباتی crRNA برای تنظیم در سطح پسارونویسی metastamiR-10b و metastamiR-126 با استفاده از تکنیک (CRISPR-C2c2 (Cas13a Design, Modeling and Computational Analysis of crRNA to Regulate MetastamiR-10b and MetastamiR-126 in Post-transcriptional Level by CRISPR-C2c2 (Cas13a) Technique 1 1 مقدمه: یکی از مهم‌ترین دلایل مرگ‌و‌میر در بین بیماران سرطانی متاستاز است. اخیراً نشان داده شده است که گروه خاصی از miRNA‌ها(microRNA) که metastamir نامیده می‌شوند دارای اثر متاستاتیکی هستند. miR-126 ارتباط مشخصی با متاستاز سرطان روده به کبد دارد. همچنین در متاستاز سرطان سینه miR-10b بیان بیش از حد پیدا می‌کند؛ بنابراین کاهش سطح بیان این miRNA‌ها می‌تواند نقش مهمی در کاهش احتمال متاستاز داشته باشد. در این تحقیق تکنیک CRISPR-C2c2(Cas13a) به منظور هدف‌گیری پیش‌سازهای miRNA جهت کاهش اثر متاستاتیکی آن‌ها مورد بررسی قرار گرفت. روش: پژوهش با استفاده از ابزارهای بیوانفورماتیک و بیوانفورماتیک ساختاری انجام شد. ساختار آنزیم C2c2 از پایگاه داده RCSB و توالی‌های miRNA و پیش‌سازهای آن‌ها از پایگاه‌های داده MirBase و Mirnamap تهیه شدند. با استفاده از سیستم برخط CRISPR-RT، crRNA‌های هدف گیرنده توالی مورد نظر طراحی و از نظر اختصاصیت مورد بررسی و ارزیابی قرار گرفتند. ساختار سه بعدی crRNA‌های طراحی شده با استفاده از نرم‌افزار RNAbuider2.8.2 شبیه‌سازی و به منظور بررسی نحوه اتصال و سطح انرژی اتصالی crRNA با آنزیم C2c2(Cas13a) از سرور Hdock استفاده شد. نتایج:crRNA طراحی شده با هدف mir-126 مشابهت ساختاری بالا با وضعیت مشاهده شده در طبیعت نشان داده و جهت­گیری مناسب حاصل شد. در مورد crRNA طراحی شده به منظور هدف­گیری mir-10b با وجود اختصاصیت بالا جهت­گیری درستی ایجاد نشد. نتیجه­گیری: بررسی توالی محور crRNA‌های طراحی شده برای ویرایش در سطح RNA کافی نیست و توصیه می­شود در کنار بررسی­های برپایه اختصاصیت، شبیه­سازی و داکینگ مولکولی به منظور دقت هر چه بیشتر انجام شود. 2 Introduction: Metastasis is one the most important causes of mortality in cancer patients. Recent studies have shown the metastatic potential of a specific group of microRNAs called metastamirs.  miR-126 is shown to be correlated with the colorectal liver metastasis. Also, overexpression of miR-10b has been reported in metastatic breast cancer.  Therefore, down regulation of these miRNAs at transcriptional level can reduce the probability of metastasis. This study analyzes targeting of miRNAs precursors using CRISPR-C2c2 (Cas13a) technique. Method: To conduct this study, we used bioinformatics and structural bioinformatics methods. The structure of C2c2 (Cas13a) enzyme was obtained from RCSB database, and the sequences of miRNAs and their precursors were collected from MirBase and Mirnamap. The crRNAs were designed, evaluated and checked for their specificity by using CRISPR-RT. The modeling of the three-dimensional structure of the designed crRNAs was performed by RNAbuilder 2.8.2 software. We used Hdock server to perform molecular docking to assess the energy level and the position of docked molecules.  Result: The crRNA designed for mir-126 showed high structural similarity with the situation observed in nature and appropriate orientation was obtained. In the case of crRNA designed to target mir-10b, despite high specificity, the correct orientation was not established. Conclusion: Sequence-based evaluation of crRNAs designed for RNA-level editing is insufficient, and it is recommended that, along with specificity, simulation and molecular docking studies, be performed for higher accuracy.   320 332 2018/05/312019/06/302019/07/92018/12/162018/12/222019/04/16 1398/1/27 2019/11/122019/08/252019/08/292019/06/12019/06/172019/07/13 1398/4/22 فاطمه ابراهیمی ترکی Fatemeh Ebrahimi Tarki کارشناسی ارشد بیوتکنولوژی میکروبی، دانشگاه الزهرا‌، دانشکده علوم زیستی، گروه بیوتکنولوژی، آزمایشگاه زیست‌شناسی محاسباتی دانشکده علوم زیستی، دانشگاه الزهرا، تهران، ایران ایران fa.ebrahimi@student.alzahra.ac.ir مهسا بوربور Mahsa Bourbour کارشناسی ارشد بیوتکنولوژی میکروبی، دانشگاه الزهرا‌، دانشکده علوم زیستی، گروه بیوتکنولوژی، آزمایشگاه زیست‌شناسی محاسباتی دانشکده علوم زیستی، دانشگاه الزهرا، تهران، ایران ایران m.bourbour@student.alzahra.ac.ir محبوبه ضرابی Mahboobe Zarrabi دکترای بیوفیزیک، استادیار، گروه بیوتکنولوژی، آزمایشگاه زیست‌شناسی محاسباتی، دانشکده علوم زیستی، دانشگاه الزهرا، تهران، ایران ایران mzarrabi@alzahra.ac.ir MetastamiR CRIPSR-C2c2(Cas13a) Metastasis inhibition CRISPR-RT crRNA -CRISPR-C2C2(Cas13a)- metastamiR‌ مهار متاستاز crRNA- CRISPR-RT 1. 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سیستم تصمیم‌یار بالینی تداخلات دارو با دارو: مزایا، موانع و چالش‌های پیش رو و استراتژی‌های غلبه بر آن‌ها Drug-Drug Interaction Clinical Decision Support Systems: Advantages, Challenges and Barriers, and Strategies to Overcome Them 1 1 یکی از مهمترین علل خطاهای پزشکی قابل پیشگیری، تداخل یک دارو با داروی دیگری است که بطور همزمان تجویز و مصرف می شوند. آمارهای معتبر حاکی از شیوع گسترده این دسته از خطاهای قابل پیشگیری در بخش های بستری و یا سرپایی سیستم های بهداشتی و درمانی کشورهای توسعه یافته و همینطور کشورهای در حال توسعه، از جمله ایران می باشد. بروز این تداخلات میتواند پیامدهای زیانبار بسیاری مانند آسیب ارگانهای حیاتی، بستری های مکرر و طولانی شدن مدت زمان بستری در بیمارستان داشته و همینطور منجر به تحمیل هزینه ها به بیمار و سیستم بهداشت و درمان گردند. سیستمهای تصمیم یار بالینی یکی از مداخلات پزشکی می باشند که می توانند در شناسایی و مدیریت به موقع و مناسب تداخلات دارو-با-دارو کمک شایانی نموده و موجب بهبود تصمیم‌گیری‌های بالینی و ارتقاء کیفیت دارودرمانی بیماران شوند. با عنایت به گسترش استفاده از سیستم های اطلاعاتی در سیستم بهداشت و درمان ایران، آشنایی با تصمیم یارهای بالینی تداخلات دارو-با-دارو، چالش های آن  و اهتمام در طراحی و گسترش استفاده روزمره از آنها می تواند کمک شایانی به کاهش خطاهای قابل پیشگیری ناشی از اینگونه تداخلات و کاهش هزینه های متعاقب آنها نماید. مقاله حاضر به مرور اجمالی ملزومات طراحی چنین سیستم هایی و موانع و چالش های پیش رو و تمهیدات لازم برای غلبه بر آنها در ایران می پردازد. 2 Drug-drug interactions (DDIs) are a common source of preventable medical errors in inpatient and outpatient settings of both developed and developing countries. When two or more drugs are simultaneously prescribed, interactions between their effects may result in preventable adverse events such as damages to vital organs, frequent hospitalizations, prolonged length of hospital stay, and increased care cost for both patients and healthcare systems. Clinical decision support systems (CDSSs) have a great potential to support care providers to identify and manage DDIs timely, and thereby, to improve the quality of decisions on medication prescriptions. In this opinion paper, we provide a concise review of efforts necessary for a successful design and development of a DDI-CDSS with particular focus on the Iranian healthcare context. We also touch upon the measures to consider in order to overcome some of the important challenges and barriers jeopardizing the design and application of DDI-CDSSs in Iran. 333 342 2018/05/312019/06/302019/07/92018/12/162018/12/222019/04/162019/08/11 1398/5/20 2019/11/122019/08/252019/08/292019/06/12019/06/172019/07/132019/08/17 1398/5/26 زهرا نیازخانی Zahra Niazkhani دکتری تخصصی انفورماتیک پزشکی، دانشیار، گروه فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران پرستو امیری Parastoo Amiri کارشناسی ارشد انفورماتیک پزشکی، مرکز توسعه تحقیقات بالینی بیمارستان امام خمینی (ره)، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران حبیب الله پیرنژاد Habibollah Pirnejad دکتری تخصصی انفورماتیک پزشکی، دانشیار، مرکز تحقیقات ایمنی بیمار، دانشگاه علوم پزشکی ارومیه، ارومیه، ایران ایران h_pirnejad@yahoo.com Clinical Decision Support System CDSS drug-drug interaction challenges advantages review سیستم های تصمیم یار بالینی تداخل دارو با دارو چالش مزایا مطالعه مروری 1. 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Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293(10):1223-38. doi: 10.1001/jama.293.10.1223##69. Weingart SN, Seger AC, Feola N, Heffernan J, Schiff G, Isaac T. Electronic drug interaction alerts in ambulatory care. Drug Saf 2011;34(7):587-93. doi: 10.2165/11589360-000000000-00000..##70. Nabovati E, Vakili-Arki H, Taherzadeh Z, Hasibian MR, Abu-Hanna A, Eslami S. Drug-drug interactions in inpatient and outpatient settings in Iran: a systematic review of the literature. Daru 2014;22(1):52. doi: 10.1186/2008-2231-22-52. ##71. Gholaminezhad S, Hadjibabaie M, Gholami K, Javadi MR, Radfar M, Karimzadeh I, et al. Pattern and associated factors of potential drug-drug interactions in both pre- and early post-hematopoietic stem cell transplantation stages at a referral center in the Middle East. Ann Hematol 2014;93(11):1913-22. doi: 10.1007/s00277-014-2122-0.##72. Rafiei H, Abdar ME, Amiri M, Ahmadinejad M. The study of harmful and beneficial drug interactions in intensive care, Kerman, Iran. Journal of the Intensive Care Society 2013;14(2):155-8. doi.org/10.1177/175114371301400214##73. Rafiei H, Esmaeili Abdar M, Moghadasi J. The Prevalence of Potential Drug Interactions Among Critically Ill Elderly Patients in the Intensive Care Unit (ICU). Salmand 2012; 6(4):14-9. [In Persian]##74. Alizadeh A, Rostamian A, Saeedpour K, Hemmati M, Khorasani Z, Mohagheghi M, et al. Drug interactions frequency in the bedridden patients in three hospitals of Tehran city. Modern Care Journal. 2011;7(3):22-7. [In Persian]##75. Namazi S, Pourhatami S, Borhani-Haghighi A, Roosta S. Incidence of Potential Drug-Drug Interaction and Related Factors in Hospitalized Neurological Patients in two Iranian Teaching Hospitals. Iran J Med Sci 2014;39(6):515-21. ##76. Nabovati E, Vakili-Arki H, Taherzadeh Z, Saberi MR, Abu-Hanna A, Eslami S. 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Risk factors for the development of adverse drug events in hospitalized patients. Pharm World Sci 2000;22(2):62-6. doi: 10.1023/A:1008721321016##64. Hazlet TK, Lee TA, Hansten PD, Horn JR. Performance of community pharmacy drug interaction software. Journal of the American Pharmaceutical Association 2001;41(2):200-4. doi: 10.1016/S1086-5802(16)31230-X##65. Abarca J, Colon LR, Wang VS, Malone DC, Murphy JE, Armstrong EP. Evaluation of the performance of drug-drug interaction screening software in community and hospital pharmacies. J Manag Care Pharm 2006;12(5):383-9. doi: 10.18553/jmcp.2006.12.5.383##66. Smithburger PL, Gill SL, Benedict NJ, Falcione BA, Seybert AL. Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings. Ann Pharmacother 2010;44(11):1718-24. doi: 10.1345/aph.1P377. ##67. van Roon EN, Flikweert S, le Comte M, Langendijk PN, Kwee-Zuiderwijk WJ, Smits P, et al. Clinical relevance of drug-drug interactions. Drug Saf 2005;28(12):1131-9.##68. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293(10):1223-38. doi: 10.1001/jama.293.10.1223##69. Weingart SN, Seger AC, Feola N, Heffernan J, Schiff G, Isaac T. Electronic drug interaction alerts in ambulatory care. Drug Saf 2011;34(7):587-93. doi: 10.2165/11589360-000000000-00000..##70. Nabovati E, Vakili-Arki H, Taherzadeh Z, Hasibian MR, Abu-Hanna A, Eslami S. Drug-drug interactions in inpatient and outpatient settings in Iran: a systematic review of the literature. Daru 2014;22(1):52. doi: 10.1186/2008-2231-22-52. ##71. Gholaminezhad S, Hadjibabaie M, Gholami K, Javadi MR, Radfar M, Karimzadeh I, et al. 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ارائه بسته فناوری برای زیرساختارهای الکترونیکی گردشگری سلامت A Technology Package for Electronic Infrastructures of Health Tourism 1 1 مقدمه: فناوری اطلاعات، بسترساز و توسعه دهنده گردشگری سلامت است. هدف مطالعه حاضر، ارائه بسته فناوری اطلاعات به عنوان زیرساختارهای الکترونیکی صنعت گردشگری سلامت می‌باشد. روش: در مطالعه مروری حاضر، برای یافتن ابعاد مختلف گردشگری الکترونیک سلامت، جستجوی موضوعی از بانک­های اطلاعاتی PubMed،Science Direct  و Scopus و نیز Google Scholar و وب سایت­های تخصصی گردشگری سلامت انجام گردید. کلیدواژه‌های جستجو شامل Medical Tourism، Health Tourism با ترکیب "AND" با Information Technology، Information and Communication Technology، e-Tourism و Electronic Tourism بود. معیارهای انتخاب مقالات، دسترسی به متن کامل مقاله و نیز پوشش جنبه‌ای از فناوری اطلاعات در حوزه گردشگری سلامت بود. از 117 مقاله استخراج شده، پس از اعمال معیارهای ورود، در نهایت 32 مقاله انتخاب شد و بسته فناوری گردشگری سلامت پس از مطالعه دقیق محتوای این مقالات ارائه گردید. نتایج: یک بسته فناوری اطلاعات که شامل انواع ابزارهای فناوری، قابلیت‌ها و ظرفیت‌های حاصل و  پیامدهای متعاقب فناوری بود، ارائه گردید. در این مدل، اینترنت به عنوان اصلی‌ترین ابزار بسترساز و در عین حال توسعه‌دهنده گردشگری سلامت؛ طراحی وب سایت‌های مراکز درمانی به عنوان مهم‌ترین قابلیت این فناوری‌ها و رقابتی و تعاملی کردن نظام سلامت از اصلی‌ترین پیامدهای استفاده از فناوری اطلاعات در حوزه گردشگری سلامت بود. نتیجه­گیری: بسته ارائه شده نشانگر وابسته بودن بسیاری از پیامدهای مثبت توسعه گردشگری سلامت به فناوری­های اطلاعات و ارتباطات است که با ایجاد پتانسیل‌های فرصت‌ساز، زمینه را جهت کسب سهمی از بازار گردشگری سلامت و در عین حال اصلاح ساختار نظام سلامت برای ارائه خدمات باکیفیت در سطح جهانی فراهم می‌کنند. 2 Introduction: Information Technology creates a foundation for developing health tourism. The purpose of the current study was to provide an information technology package for electronic infrastructures of health tourism. Methods: In this review study, in order to find various dimensions of health tourism, articles related to electronic health tourism were searched in PubMed, Science Direct and Scopus databases, as well as Google Scholar and specific websites of health tourism. Searched key words included Medical Tourism, Health Tourism "AND" Information Technology, Information and Communication Technology, e-Tourism and Electronic Tourism. Criteria for selecting articles were access to their full texts as well as covering some aspects of information technology in the field of health tourism. The search yielded 117 articles, of which 32 articles were selected finally. The contents of these articles were carefully studied and the technology package of health tourism was presented. Results: Based on the findings, an information technology package was developed that included different types of technology tools, capabilities, and outcomes of information technology applications in health tourism. In this package, Internet was the main tool for developing health tourism, websites of health centers were the most important capability of these technologies, and creating a competitive and interactive health system was the main consequence of information technology application in the field of health tourism. Conclusion: The designed package illustrates the dependence of positive outcomes of health tourism development to information-communication technologies. These tools provide the potential for gaining a share of the health tourism market and reform the health system structure to deliver high quality services in a global level. 343 357 2018/05/312019/06/302019/07/92018/12/162018/12/222019/04/162019/08/112019/06/14 1398/3/24 2019/11/122019/08/252019/08/292019/06/12019/06/172019/07/132019/08/172019/08/12 1398/5/21 زهرا مستانه Zahra Mastaneh دکترای تخصصی مدیریت اطلاعات سلامت، استادیار، مرکز تحقیقات عوامل اجتماعی در ارتقاء سلامت خلیج‌فارس، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران ایران zmastaneh@gmail.com علی موصلی Ali Mouseli دکترای تخصصی مدیریت خدمات بهداشتی، استادیار، مرکز تحقیقات عوامل اجتماعی در ارتقاء سلامت خلیج‌فارس، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران ایران mouseli136025@gmail.com محمدحسین حیوی حقیقی Mohammad Hosein Hayavi Haghighi دکترای تخصصی مدیریت اطلاعات سلامت، استادیار، گروه مدیریت و فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران ایران hayavi2005@gmail.com محمدصادق گل وردی Mohammad Sadegh Golvardi کارشناسی ارشد بیهوشی، مربی، گروه اتاق عمل و هوشبری، دانشکده پیراپزشکی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران ایران golvardi@hums.ac.ir محمدمهدی بلالی Mohammad Mehdi Balali دانشجوی کارشناسی فناوری اطلاعات سلامت، گروه مدیریت و فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی هرمزگان، بندرعباس، ایران ایران balali5459@gmail.com Electronic infrastructures Internet Health center websites Interactive health system Health tourism زیرساختارهای الکترونیکی اینترنت وب سایت های درمانی نظام سلامت تعاملی گردشگری سلامت 1. 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نقش سلامت الکترونیک در ابتدای دوره بحران بیماری کرونا: مرور نظام‌مند مستندات The Role of Electronic Health During the Covid-19 Crisis: A Systematic Review of Literatures 1 1 مقدمه: در اواخر سال 2019 بیماری همه‌گیر کرونا در شهر ووهان کشور چین شیوع پیدا کرد و به سرعت تمامی جهان را درگیر خود کرد. از طرفی دیگر شایان ذکر است که فناوری اطلاعات در نظام سلامت کنونی نقش بسزایی دارد؛ بنایراین هدف از این مطالعه تعیین نقش سلامت الکترونیک در طول شیوع بحران بیماری کرونا بود. روش: در این مطالعه مروری، مطالعات انجام شده از دسامبر 2019 تا 7 مارس 2020 با جستجوی کلمات کلیدی در پایگاه داده‌های الکترونیک PubMed، Web of Science و Scopus جستجو شدند، همچنین از موتور جستجوی Google استفاده گردید. در مجموع از 47 مطالعه مرتبط یافت شده، 21 مقاله براساس عنوان پژوهش این مطالعه شناسایی شد. پس از اعمال معیارهای ورود و خروج، در نهایت 5 مقاله اصلی و 3 گزارش برای بررسی انتخاب شدند. نتایج: در مطالعات بررسی شده به نقش مؤثر سلامت الکترونیک در تشخیص بیماران مبتلا به کرونا، چگونگی شیوع این بیماری، آموزش‌های مربوط به پیشگیری و شناسایی نقاط پرخطر اشاره شد. از جمله فناوری‌های پرکاربرد می‌توان به پزشکی از راه دور، الگوریتم‌های یادگیری ماشین، یادگیری عمیق، شبکه‌های عصبی و سیستم‌های اطلاعات جغرافیایی اشاره کرد. نتیجه­گیری: نتایج حاصل از این بررسی نشان داد که دولت‌ها و متخصصان انفورماتیک پزشکی می‌توانند از تجارب نقش سلامت الکترونیک در شیوع بحران بیماری کرونا برای مقابله با هر گونه بیماری همه‌گیر در آینده درس بگیرند تا مانع از هر گونه تهدید جانی ملل، رکود اقتصادی و در نهایت پاسخ سریع در مواقع فوق بحرانی گردند.   2 Introduction: In late 2019, Covid-19 Outbreak started in Wuhan, China and quickly spread through whole world. On the other hand, health information technology plays an important role in the current health system. Therefore, the aim of this systematic review investigates the role of electronic health during the outbreak of Covid-19 Crisis. Method: In this review study, studies from December 2019 to March 7, 2020 were searched for keywords and their equivalent in the "Medical Subject Headings (MeSH)" in the PubMed, Web of Science and Scopus databases also google search was used. From a total of 47 studies, 21 articles were identified relevant based on our research purpose. After applying the inclusion and exclusion criteria, finally five main articles and three reports were selected for review. Results: Studies evaluated the effective role of information technology in the diagnosis of Covid-19, prevalence, preventive, education and identification of high-risk areas. Among the technologies used were telemedicine, machine learning algorithms, deep learning, neural networks, and geographic information systems. Conclusion: The results of this study showed that governments and medical informatics professionals can learn from the experiences of the role of technology in preventing Covid-19 outbreak for any future epidemic to prevent any threat to the lives of nations, the recession, and ultimately the immediate response in times of crisis.   358 367 2018/05/312019/06/302019/07/92018/12/162018/12/222019/04/162019/08/112019/06/142020/03/16 1398/12/26 2019/11/122019/08/252019/08/292019/06/12019/06/172019/07/132019/08/172019/08/122020/03/16 1398/12/26 پرستو امیری Parastoo Amiri دانشجوی دکتری انفورماتیک پزشکی، کمیته تحقیقات دانشجویی، دانشگاه علوم پزشکی کرمان،کرمان، ایران ایران parastu.amirii@yahoo.com Electronic Health Medical Informatics. Outbreak Crisis Covid-19 Systematic Review سلامت الکترونیک انفورماتیک پزشکی شیوع بحران بیماری کرونا مرور نظام‌‌مند 1. 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