یک گام موفق به سوی کاربرد انفورماتیک در پرستاری در ایران A Successful Step towards Application of Informatics in Nursing in Iran 1 1 مقدمه: امروزه با توجه به توسعه روز افزون علم پرستاری و افزایش تقاضا برای بهبود و ارتقای کیفیت مراقبت‌های پرستاری، ضرورت تلفیق علم انفورماتیک با علم پرستاری، به جهت مدیریت صحیح اطلاعات پرستاری، بیش از پیش خود نمایی می‌کند؛ بنابراین مطالعه حاضر با هدف توسعه سیستم الکترونیک اطلاعات پرستاری به عنوان گامی در راستای تلفیق علم انفورماتیک با علم پرستاری‌، انجام شد. روش: مطالعه با استفاده از رویکرد اقدام‌پژوهی تکنیکی انجام شد. مراحل مطالعه شامل توسعه سیستم الکترونیک اطلاعات پرستاری، اجرا و ارزشیابی آن بود. سیستم در بخش مراقبت ویژه نوزادان با مشارکت پرستاران در سال 1395 اجرا شد. نتایج کمی ارزشیابی سیستم با استفاده از روش‌های آمار توصیفی و داده‌های کیفی حاصل از مصاحبه‌ها با استفاده از روش تحلیل محتوا و کد‌گذاری قراردادی مورد تجزیه و تحلیل قرار گرفت. نتایج: نتایج پرسشنامه‌های رضایت‌سنجی حاکی از رضایت پرستاران از اجزا و کاربرد سیستم الکترونیک بود. سطح اطلاعات دارویی و اطلاعات مربوط به فرآیند پرستاری پرستاران نیز بعد از استفاده از سیستم افزایش یافت. تحلیل محتوای یافته‌های کیفی نیز منجر به استخراج طبقات اصلی ارتقاء کیفیت مراقبت‌ها، بهبود فرآیند مستندسازی، ارتقاء حرفه‌ای، آموزش و یادگیری مداوم، تلفیق تئوری و عمل در کاربرد فرآیند پرستاری، موانع انسانی و تجهیزاتی شد. نتیجه‌گیری: نتایج مثبت مطالعه حاضر نشان داد که توجه به مواردی مثل طراحی سیستم الکترونیک بومی مبتنی بر نیازهای پرستاران و مشارکت فعال ذینفعان در همه مراحل انجام مطالعه، توانست گامی هرچند کوچک ولی موفق به سوی کاربرد مؤثر از علم انفورماتیک در علم پرستاری بردارد. 2 Introduction: Nowadays, due to the increasing development of nursing science and demand for improving and enhancing the quality of nursing care, the necessity of integrating informatics with Nursing Science for proper management of nursing information flaunts more than ever. Therefore, the present study aimed to develop the electronic information system of nursing as a step towards the integration of informatics science with nursing science. Methods: The present study was conducted using technical action research approach. The main stages of the study were the development of a nursing electronic information system, its implementation, application and evaluation in a neonatal intensive care unit in 2016. Descriptive statistics was used for the analysis of quantitative data and qualitative data obtained from interviews were analysed using content analysis and coding. Results: The results of Satisfaction Scale Questionnaire showed satisfaction of nurses with the components and applications of the electronic system. Nurses' information about drugs and the process of nursing increased after using the system. Content analysis of qualitative findings also led to exploit the main categories of enhancing the quality of care, improving the documentation process, professional development, continuing education and learning, the integration of theory and practice in the application of the nursing process, human and equipment barriers. Conclusion: According to the obtained results, paying attention to the design of a native system based on the needs of nurses and the participation of the beneficiaries could take a small but successful step toward effective use of informatics science in nursing. 71 83 2017/05/14 1396/2/24 2017/09/6 1396/6/15 مریم ورزش نژاد Maryam Varzeshnejad دکترای پرستاری، گروه پرستاری کودکان، دانشکده پرستاری و مامایی، دانشگاه علوم پزشکی شهید بهشتی، تهران، ایران. ایران m_varzeshnejad@yahoo.com محبوبه نم نباتی Mahboobeh Namnabati دانشگاه علوم پزشکی اصفهان- دانشکده پرستاری و مامایی ایران arash.1965@yahoo.com فریبا طالقانی Faribai Taleghani دانشگاه علوم پزشکی اصفهان- دانشکده پرستاری و مامایی ایران fr.mhr.2014.@gmail.com Electronic Nursing Information System Action Research Neonatal Intensive Care Nursing Informatic سیستم الکترونیک اطلاعات پرستاری اقدام پژوهی مراقبت ویژه نوزادان پرستاری انفورماتیک 1- Hazmy I A, Maryati MY, Shahrul AM, The application of Ontology in a Clinical Decision Support System for Acute Postoperative Pain Management. 2011 International Conference on Semantic Technology and Information Retrieval. 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Assessment of Supportive and educative role of decision support systems in nursing: A Review article, Journal of Education and Ethics in Nursing, 2015, 4(2): 1-14.##9- Yuan MJ, Finley GM, Long J, Mills C, Johnson RK. Evaluation of user interface and workflow design of a bedside nursing clinical decision support system. Interactive journal of medical research, 2013.2(1).##10- Valizadeh F, Ghasemi SF, Nagafi SS, Delfan B, Mohsenzadeh A. Errors in Medication Orders and the Nursing Staff's Reports in Medical Notes of Children. Iranian Journal of Pediatrics. 2008.18(Suppl 1):33-40##11- Board N. Medication Safety in the Neonatal Intensive Care Unit. National Association of Neonatal Nurses. 2011.##12- Kim H, Dykes PC, Thomas D, Winfield LA, Rocha RA. A closer look at nursing documentation on paper forms: Preparation for computerizing a nursing documentation system. Computers in biology and medicine. 2011;41(4):182-9.##13- Richardson JC, Ice P. 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Jyväskylä Studies in Computing. 2010; 125.##20- Johnson M, Moorhead S, Bulechek G, Howard Butcher F, Maas M, Swanson E, .NOC and NIC Linkages to NANDA-I and Clinical Conditions Supporting Critical Reasoning and Quality Care. (BOOK). 2012 by Mosby, Inc., an affiliate of Elsevier Inc.##21- Thomas E. Young, Barry Mangum.NEOFAX.(Book).2016. Thomas Reuters; 25 edition .##22- Rantz MJ, Alexander G, Galambos C, Flesner MK, Vogelsmeier A, Hicks L, Scott-Cawiezell J, Zwygart-Stauffacher M, Greenwald L. The use of bedside electronic medical record to improve quality of care in nursing facilities: a qualitative analysis. CIN: Computers, Informatics, Nursing. 2011.1; 29(3):149-56.##23- Su K-W, Liu C-L. A mobile Nursing Information System based on human-computer interaction design for improving quality of nursing. 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بهینه‌سازی و تحلیل هزینه‌ای درمان دارویی دیابت نوع 2 برای پیشگیری از هایپوگلیسمی با رویکرد فرآیند تصمیم ‌مارکوف Optimizing and Cost-effective Analysis of Medication Treatment of Type 2 Diabetes for Prevention of Hypoglycemia Using Markov Decision Process 1 1 مقدمه: ابتلا به بیماری دیابت و تأثیر آن بر روی عملکردهای فردی و اجتماعی بیماران، هزینه‌های بالایی برای جوامع ایجاد می‌کند. با کنترل قندخون بیماران دیابتی می‌توان از بروز عوارض ناگوار این بیماری جلوگیری نمود. مطالعه پیش رو با هدف بهینه‌سازی درمان دارویی بیماری دیابت نوع 2 انجام شده است. روش: در این مطالعه کاربردی- پیمایشی، زمان‌بندی و توالی درمان‌های دارویی کنترل قندخون با رویکرد فرآیند تصمیم‌گیری مارکوف مدل می‌شود. برای نزدیکی مدل به واقعیت، مباحث هزینهای، ارزش زمانی پول و تورم در نظر گرفته می‌شود تا درمان دارویی بیماری دیابت، به صورت اقتصادی برنامه‌ریزی شود. مصرف داروهای ضد دیابت، باعث بروز هایپوگلیسمی در بیماران دیابتی با سنین بالا می‌شود. از این رو، رویکرد پیشگیری از هایپوگلیسمی در سنین بالا، بر روی مدل پیشنهادی اعمال و تأثیر آن مورد تجزیه و تحلیل قرار داده شد. عملکرد مدل پیشنهادی و کارآیی سیاست بهینه حاصل، با گایدلاین‌‌های بالینی مورد مقایسه قرار گرفت. به منظور بررسی رفتار مدل، تجزیه و تحلیل حساسیت بر روی پارامترهای هزینه‌ای نیز صورت پذیرفته است. نتایج: مدل پیشنهادی عملکرد بهتری نسبت به گایدلاین بالینی بیماری دیابت برای کنترل قندخون نشان داده است. مباحث هزینه‌ای بر روی برنامه درمانی تأثیرگذار بوده و لازم است که برای برنامه‌ریزی اقتصادی درمان دارویی بیماری دیابت لحاظ شود. نتیجه گیری: با بهینه‌سازی تصمیم‌های درمان دارویی بیماری دیابت می‌توان نه تنها دوره عمر با کیفیت بیماران را بهبود بخشید، بلکه از هزینه‌های بالای آن بر روی نظام سلامت کشورها جلوگیری نمود. 2 Introduction: Type 2 diabetes (T2D) imposes high expenses on societies, due to its effects on individual and social functions of patients. Adverse complications of this disease can be prevented through controlling patient's blood sugar level. The purpose of this study was to optimize the medication treatment of type 2 diabetes. Methods: In this applied survey, Markov decision process (MDP) was used to model the scheduling and sequencing of T2D medication treatments. In order to have a model close to reality, expense issues, time value of money (TVM) and inflation were considered and it was tried to plan a cost-effective treatment program. Since anti-diabetic drugs cause hypoglycemia in elderly patients, a hypoglycemia preventive approach was applied on the proposed model and its effect was analyzed. The function of proposed model and efficiency of the obtained optimal policy were compared with clinical guidelines. Moreover, in order to address the model behavior, sensitivity analysis on parameters related to treatment costs was also conducted. Results: The proposed model showed better performance in glycemic control compared to clinical guidelines. Treatment cost parameters affected treatment program. Conclusion: In general, optimizing the medication treatment of T2D not only can improve patients' life period with quality, but also can decline high costs imposed on societies’ healthcare systems. 84 97 2017/05/142017/05/10 1396/2/20 2017/09/62017/07/2 1396/4/11 مریم اقبالی زارچ Maryam Eghbali-Zarch دانشجوی دکتری مهندسی صنایع، دانشکده مهندسی صنایع، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران ایران m.eghbali@ut.ac.ir رضا توکلی مقدم Reza Tavakkoli Moghaddam دانشگاه تهران ایران tavakoli@ut.ac محمد مهدی سپهری Mohammad Mehdi Sepehri دانشگاه تربیت مدرس ایران fesfahanian@sina.tums.ac فاطمه اصفهانیان Fatemeh Esfahanian دانشگاه علوم پزشکی تهران ایران mehdi.sepehri@modares.ac امیر آذرون Amir Azaron دانشگاه سایمون فریزر ایران aazaron@sfu.c Medical decision making Type 2 Diabetes Hypoglycemia Optimization Markov decision process تصمیم‌گیری درمان پزشکی دیابت نوع 2 هایپوگلیسمی بهینه‌سازی فرآیند تصمیم مارکوف 1. International Diabetes Federation. IDF Diabetes, 7 ed. 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The structure of optimal statin initiation policies for patients with Type 2 diabetes. IIE Transactions on Healthcare Systems Engineering. 2011;1(1):49-65.##22. Mason JE, Denton BT. A comparison of decision-maker perspectives for optimal cholesterol treatment. IBM Journal of Research and Development 2012; 56(5):8-12.##23. Zhang Y, McCoy RG, Mason JE, Smith SA, Shah ND, Denton BT. Second-line agents for glycemic control for type 2 diabetes: are newer agents better? Diabetes Care 2014;37(5):1338-45.##24. Alikar N, Mousavi SM, Ghazilla RA, Tavana M, Olugu EU. A bi-objective multi-period series-parallel inventory-redundancy allocation problem with time value of money and inflation considerations. Computers & Industrial Engineering 2017; 104:51-67.##25. Bellman R. Dynamic programming and Lagrange multipliers. Proc Natl Acad Sci USA 1956; 42(10): 767–9.##26. Puterman ML. Markov decision processes: discrete stochastic dynamic programming. USA: John Wiley & Sons; 2014.##27. 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Lancet 2010;375(9723):1365-74.##32. Ouwens M, Wollersheim H, Hermens R, Hulscher M, Grol R. Integrated care programmes for chronically ill patients: a review of systematic reviews. Int J Qual Health Care 2005;17(2):141-6.##33. Choosing Drugs: Oral Diabetes Drugs, Consumer Reports and Consumer Reports Best Buy Drugs. 2016. [2017 Jun 14 Available from: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&cad=rja&uact=8&ved=0ahUKEwi6nYeVlq_XAhWJIuwKHdA7ATMQFgg6MAQ&url=http%3A%2F%2Fwww.tcyh.org%2Fadmin%2Fimages%2Fdoc_uploads%2FBBD-Diabetes-2pg.pdf&usg=AOvVaw0ua1sPtJ8L7yoSDfxctB4L##34. Yeaw J, Lee WC, Aagren M, Christensen T. Cost of self-monitoring of blood glucose in the United States among patients on an insulin regimen for diabetes. J Manag Care Pharm 2012;18(1):21-32. ##35. Bennett WL, Maruthur NM, Singh S, Segal JB, Wilson LM, Chatterjee R, et al. Comparative effectiveness and safety of medications for type 2 diabetes: an update including new drugs and 2-drug combinations. 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بررسی عوامل مؤثر بر یادگیری الکترونیکی در رشته‌های علوم پزشکی The Effective Factors on Success of E-learning in Medical Sciences Fields 1 1 مقدمه: امروزه استفاده از فناوری اطلاعات در آموزش به جهت برخورداری از مزایای بسیار از جمله امکان استفاده در هر زمان و مکان، از مهم‌ترین اهداف بسیاری از مؤسسات آموزشی در سراسر جهان و ایران می‌باشد. جهاد دانشگاهی واحد علوم پزشکی تهران به عنوان یکی از برگزارکنندگان دوره‌های آموزشی تخصصی، اقدام به برگزاری برخی دوره‌های آموزشی الکترونیکی نموده است. در این پژوهش سعی داشتیم که نقش هر یک از عوامل مؤثر در آموزش الکترونیکی را در میزان موفقیت این نوع آموزش مورد بررسی قرار دهیم. روش: در این پژوهش توصیفی میزان تأثیر هریک از این شش عامل از دیدگاه چهار گروه از شرکت‌کنندگان دوره‌ها، اساتید، کارکنان و مدیران جهاد دانشگاهی، با استفاده از پرسشنامه محقق ساخته شامل 39 سؤال در مقیاس لیکرت، در سال تحصیلی 90-1389 مورد بررسی قرار گرفته است. پرسشنامه از طریق پست الکترونیک برای 420 نفر ارسال شد و در مجموع 188 نفر در این پژوهش مشارکت کردند. داده‌های جمع‌آوری شده با استفاده از نرم‌افزار SPSS و روش‌های آمار توصیفی و استنباطی تحلیل شدند. نتایج: نتایج به دست آمده از دیدگاه چهار گروه مشارکت کننده بیانگر اهمیت بالای کلیه عوامل مورد بررسی در پژوهش به ترتیب مدیریت، محتوای آموزشی، امکانات مورد نیاز، یاد دهنده، قوانین و مقررات و یادگیرنده می‌باشد. نتیجه‌گیری: هرچند که تفاوت بین عوامل مؤثر ناچیز است؛ اما همت مضاعف مسئولان در برطرف کردن کاستی‌های و نقاط ضعف سه عامل اصلی و مؤثر مدیریت، محتوای آموزشی و امکانات مورد‌نیاز در موفقیت آموزش الکترونیک مورد‌نیاز است. 2 Introduction: Today, the use of information technology in education, is one of the most important goals of many educational institutions around the world and Iran, due to its advantages including the possibility to be used at any time and place. ACECR branch of Tehran University of Medical Sciences, as one of the organizers of specialized training courses, has already organized some e-learning courses. In this research, we tried to examine the role of each of the effective factors in e-learning in the success rate of this type of education. Methods: In this descriptive study, the effect of six factors, from the perspective of the four groups of participants, professors and ACECR (Iranian academic center for education, culture and research) staff and managers, was measured in 2010-2011, through using a researcher-made questionnaire including 39 questions on Likert scale. The questionnaire was sent via e-mail to 420 people and a total of 188 people participated in this study. The collected data were analyzed through SPSS software and using descriptive statistics and inferential statistics. Results: The results indicated the importance of all studied factors with the order of management, educational content, facilities, teachers, rules and learner. Conclusion: Although the difference between the effective factors is negligible, the extra effort of authorities to remove the shortcomings and weaknesses of the three main and effective factors of management, educational content and facilities needed for the success of e-earning is required 98 107 2017/05/142017/05/102017/06/19 1396/3/29 2017/09/62017/07/22017/09/2 1396/6/11 نجمه ناظری Najme Nazeri کارشناسی ارشد مدیریت فناوری اطلاعات، مربی، گروه انفورماتیک پزشکی، مرکز تحقیقات سرطان پستان، پژوهشکده معتمد جهاد دانشگاهی، تهران، ایران. ایران najme.nazeri@gmail.com سارا دری Sara Dorri پژوهشکده معتمد جهاد دانشگاهی ایران dorri.sara@gmail.com علیرضا آتشی Alireza Atashi پژوهشکده معتمد جهاد دانشگاهی ایران smatashii@gmail.com E-learning E-Training Success Factors یادگیری الکترونیکی آموزش الکترونیکی عوامل موفقیت (1) Anarinejad, A. A Conceptual Framework Development for E-learning Program. (2010). Journal of Technology of education. Vol. 4, No. 3, PP: 191-201.##(2) Anderson, T. Elloumi ، F. Theory and practice of online learning. (2004). PP: 31-54.##(3) Sharifnia, H. E-Learning in Medical Education. (2007). Mashhad University of Medical science. PP: 37-41.##(4) Samadi, V. Effective Factors on Student’s Learning Quality. (2010). 5th National and 2nd International Conference on E-Learning and E-Teaching. ##(5) Samadi, V. Identification of E-Learning System’s Success Key Factors in Iranian Universities. (2010). 5th National and 2nd International Conference on E-Learning and E-Teaching. ##(6) Abedi, A. E-Education Essentials. Islamic Azad University. PP:18-19##(7) Ghorbani, F. Topic Map in E-Learning Researches in 2001-2010. (2010). 5th National and 2nd International Conference on E-Learning and E-Teaching.##(8) Masoumi, D. Quality Improvement in Virtual Universities. (2007). 2nd Conference on E-Learning.##(9) Yaghoubi, J. E-Learning Models Analyze. (2007). 2nd Conference on E-Learning.##(10) Ellis, Robert& Ginns, paul& Piggott, Leanne. (2009). E-learning in higher education: some key aspects and their relationship to approaches to study. Higher Education Research & Development, june 2009, PP 303-318.##(11) Frazeen, B. Technology to Enhance the Learning Experience. available at: clomedia.com/content/templates/clo_feature.asp?articleid, 2006. PP 2-5.##(12) Govindasamy, T. (2002). Successful Implementation of Elearning ##(13) Khan, B. H. (2005). Managing e-learning: Design, delivery, implementation, and evaluation. Hershey, PA: Information SciencePublishing. (Website: http://BooksToRead.com/elearning), ##(14) Muyinda, P. (2007). Mlearning:pedagogical,technical and organizational hypes and realities. Department of Information systems, Facualty of Computing and IT,Emerald Group publishing limited ISSN1065-0741,Vol. 24, No. 2, pp. 97-107. ##(15) Selim, Hassan. (2005). M. Critical success factors for elearning acceptance: Confirmatory factor models . PP 13-17 ##(16) Starr, S. (2003). Application of mobile technology in learning & teaching: m-learning. learning & teaching enhancement unit(LTEU), Briefing paper. ##(1) Anarinejad, A. A Conceptual Framework Development for E-learning Program. (2010). Journal of Technology of education. Vol. 4, No. 3, PP: 191-201.##(2) Anderson, T. Elloumi ، F. Theory and practice of online learning. (2004). PP: 31-54.##(3) Sharifnia, H. E-Learning in Medical Education. (2007). Mashhad University of Medical science. PP: 37-41.##(4) Samadi, V. Effective Factors on Student’s Learning Quality. (2010). 5th National and 2nd International Conference on E-Learning and E-Teaching. ##(5) Samadi, V. Identification of E-Learning System’s Success Key Factors in Iranian Universities. (2010). 5th National and 2nd International Conference on E-Learning and E-Teaching. ##(6) Abedi, A. E-Education Essentials. Islamic Azad University. PP:18-19##(7) Ghorbani, F. Topic Map in E-Learning Researches in 2001-2010. (2010). 5th National and 2nd International Conference on E-Learning and E-Teaching.##(8) Masoumi, D. Quality Improvement in Virtual Universities. (2007). 2nd Conference on E-Learning.##(9) Yaghoubi, J. E-Learning Models Analyze. (2007). 2nd Conference on E-Learning.##(10) Ellis, Robert& Ginns, paul& Piggott, Leanne. (2009). E-learning in higher education: some key aspects and their relationship to approaches to study. Higher Education Research & Development, june 2009, PP 303-318.##(11) Frazeen, B. Technology to Enhance the Learning Experience. available at: clomedia.com/content/templates/clo_feature.asp?articleid, 2006. PP 2-5.##(12) Govindasamy, T. (2002). Successful Implementation of Elearning ##(13) Khan, B. H. (2005). Managing e-learning: Design, delivery, implementation, and evaluation. Hershey, PA: Information SciencePublishing. (Website: http://BooksToRead.com/elearning), ##(14) Muyinda, P. (2007). Mlearning:pedagogical,technical and organizational hypes and realities. Department of Information systems, Facualty of Computing and IT,Emerald Group publishing limited ISSN1065-0741,Vol. 24, No. 2, pp. 97-107. ##(15) Selim, Hassan. (2005). M. Critical success factors for elearning acceptance: Confirmatory factor models . PP 13-17 ##(16) Starr, S. (2003). Application of mobile technology in learning & teaching: m-learning. learning & teaching enhancement unit(LTEU), Briefing paper. ## ##

تشخیص بیماری تب کریمه‌کنگو با استفاده از درخت تصمیم C4.5 Detection of Crimean-Congo Fever Using C4.5 Decision Tree 1 1 مقدمه: با شروع فصل تابستان، بیماری بین انسان و حیوان، یعنی تب کریمه‌کنگو به سرعت شیوع پیدا می‌کند. تشخیص این بیماری با استفاده از آزمایش‌های لازم، در کمترین حالت زمانی حدود یک هفته به طول می‌انجامد. روش‌های داده‌کاوی و یادگیری ماشین متعددی برای ایجاد مدل‌های پیشگویی‌کننده جهت شناسایی افراد در معرض خطر وجود دارد. در این پژوهش از درخت تصمیم C4.5 به دلیل سادگی و کارآمدی‌‌اش به منظور تشخیص این بیماری استفاده شده است. روش: این پژوهش از نوع کاربردی و توصیفی است. در این پژوهش از داده‌های مربوط به افراد مظنون به بیماری تب کریمه‌کنگو استفاده شد. این داده‌ها در یک دوره‌ 4 ساله از سال 1393 از مراکز درمانی کشور جمع‌آوری شد. این پایگاه‌ داده شامل 965 رکورد و 28 ویژگی است. ابتدا با استفاده از روش انتخاب ویژگی برنامه‌نویسی درجه دو، متغیرهای مؤثر و تأثیرگذار بر مدل انتخاب و سپس درخت تصمیم C4.5 با به ‌کارگیری متغیرهای ورودی و تعیین متغیر هدف ایجاد گردید. تجزیه‌و‌تحلیل داده‌ها به کمک نرم‌افزار Matlab صورت گرفت. نتایج: با توجه به مدل مشخص شد که متغیرهایی همچون تب، خون‌ریزی، شروع ناگهانی علائم، افزایش آنزیم‌های کبدی، افزایش بیلی روبین توتال، کاهش هموگلوبین، Hematuria، Leukocytosis، Proteinuria و Leukopenia بیشترین تأثیر را در تشخیص به این بیماری دارند. نتیجه گیری: نتایج نشان می‌دهد که معیار حساسیت مدل پیشنهادی، 95‌% و معیار تشخیص آن 50‌% است که در مقایسه با مطالعات انجام‌شده دیگر در حوزه داده‌کاوی پزشکی، از اثربخشی قابل قبولی در تشخیص این بیماری برخوردار است. 2 Introduction: The prevalence of Crimean-Congo fever, a common disease between human and animal, shows an increasing rate by coming summer season. Detection of this disease by the use of necessary tests, lasts at least about one week. There are several data mining and machine learning techniques to create predictive models for identifying at risk people. In this study, C4.5 decision tree method has been used due to its simplicity and efficiency. Methods: In this applied descriptive study, data related to suspected cases of Crimean-Congo fever were used. These data have been collected from health centers of Iran in a four-year period since 2014 and contained 965 records with 29 features. First, by using the quadratic programming feature selection method, the variables which were effective on the model were selected and then, the C4.5 decision tree model was created through using input variables and determining the target variable. Data analysis was performed through Matlab software. Results: According to the applied model, it was found that fever, bleeding, sudden onset of symptoms, increased liver enzyms, increased total Bilirubin, decreased Hemoglobin, Hematuria, Leukocytosis, Proteinuria and Leukopenia have the greatest impact in the diagnosis of this disease. Conclusion: According to the obtained results, the sensitivity of the proposed model is 95% and its specificity is 50%. Therefore, this model showed acceptable efficiency in diagnosing this disease in comparison with other studies done in medical data mining field. 108 121 2017/05/142017/05/102017/06/192017/07/23 1396/5/1 2017/09/62017/07/22017/09/22017/09/18 1396/6/27 رضا اسماعیلی گوهری Reza Esmaeeli Gohari موسسه آموزش عالی بهمنیار ایران narcre@gmail.com الهام اسماعیلی گوهری Elham Esmaeeli Gohari 2. کارشناس ارشد مهندسی کامپیوتر، گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه یزد، یزد، ایران. ایران g.elhamesmaeeli@gmail.com مهدی شفیعی Mehdi Shafiei دانشگاه علوم پزشکی کرمان ایران kmushafiei@yahoo.com Medical decision support system Disease diagnosis Crimean-Congo hemorrhagic fever C4.5 Decision tree سیستم تصمیم‌یار پزشکی تشخیص بیماری تب کریمه‌کنگو درخت تصمیم C4.5 1. World Health Organization(WHO). Crimean-Congo haenorrhagic fever (CCHF). [cited 2017 Aug 2]. Available http://www.who.int/csr/disease/crimean_congoHF/en.##2. Boo S, Froelicher ES. 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Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. 3th ed. San Francisco: Morgan Kaufmann; 2011.##14. Tanner L, Schreiber M, Low JG, Ong A, Tolfvenstam T, Lai YL, et al. Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Negl Trop Dis 2008;2(3):e196. ##15. Shaukat K, Masood N, Mehreen S, Azmeen U. dengue fever prediction: a data mining problem. J Data Mining Genomics Proteomics 2015; 6(3):181.##16. Saha S, Saha S. Combined committee machine for classifying dengue fever. International Conference on Microelectronics, Computing and Communications (MicroCom); 2016 Jan 23-25; Durgapur, India: IEEE; 2016. p. 1-6.##17. Saikia D, Dutta JC. Early diagnosis of dengue disease using fuzzy inference system: International Conference on Microelectronics, Computing and Communications (MicroCom); 2016 Jan 23-25; Durgapur, India: IEEE; 2016. p. 1-6.##18. Olanow CW, Watts RL, Koller WC. An algorithm (decision tree) for the management of Parkinson's disease (2001): treatment guidelines. Neurology 2001;56(11 Suppl 5):S1-S88.##19. Cai Z, Gu J, Chen HL. A new hybrid intelligent framework for predicting Parkinson’s disease. IEEE Access 2017; 5: 17188-200.##20. Chen HL, Wang G, Ma C, Cai ZN, Liu WB, Wang SJ. An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳ s disease. Neurocomputing 2016; 131-44.##21. Wang W, Richards G, Rea S. Hybrid data mining ensemble for predicting osteoporosis risk: 27th IEEE Annual International Conference of the Engineering in Medicine and Biology Society; 2005 Sep 1-4; Shanghai, China, IEEE; 2005. p. 886-9.##22. Gao Z, Hong W, Xu Y, Zhang T, Song Z, Liu J. Osteoporosis Diagnosis Based on the Multifractal Spectrum Features of Micro-CT Images and C4.5 Decision Tree. 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بررسی عملکرد بخش مدیریت اطلاعات سلامت بیمارستان ولیعصر (عج) The Performance of Health Information Management Department in Valiassr Hospital 1 1 مقدمه: با توجه به اهمیت واحد مدیریت اطلاعات سلامت در بهبود ارائه خدمات بهداشتی و درمانی مطالعه حاضر با هدف بررسی عملکرد بخش مدیریت اطلاعات سلامت بیمارستان ولیعصر (عج) شهر تهران در سال 1396 انجام گردیده است. روش: مطالعه حاضر یک مطالعه کاربردی از نوع کمی-کیفی است که در سال 1396 انجام شد. برای جمع‌آوری داده‌ها در بخش کمی از یک چک لیست روا و پایا استفاده گردید و در بخش کیفی تعداد 8 مصاحبه انجام گردید. برای تحلیل داده‌های بخش کمی از نرم‌افزارExcel و برای بخش کیفی از نرم‌افزار MAXQDA استفاده گردید. نتایج: نتایج مطالعه نشان داد که واحد کدگذاری عملکرد ضعیف‎تری نسبت به سایر واحد‎ها داشته ‎است (2/37 از 3‌). مشکلات بخش مدیریت اطلاعات سلامت بیمارستان ولیعصر(عج) نیز در پنج دسته اصلی سازمانی، نیروی انسانی، مالی، زیرساختی و سخت‎افزاری و دانش و تخصصی طبقه‎بندی می‎شوند، از مهم‌ترین اقدامات انجام شده هم می‎توان به اسکن کردن اوراق اصلی اشاره نمود. نتیجه‌گیری: با توجه به نتایج مطالعه حاضر پیشنهاد می‎شود مسئولین امر در توجه ویژه و داشتن دید تخصصی به بخش مدیریت اطلاعات سلامت اهتمام ورزند. 2 Introduction: With regard to the importance of Health Information Management in improvement of health care services, this study was done to determine the performance of Health Information Management Department of Valiasr hospital of Tehran city in 2017. Methods: This mix-method study was done in 2017. For data collection in quantitative study, a valid and reliable check list and in qualitative study, 8 interviews were used. Excel 2013 and MAXQDA were used for the analysis of quantitative and qualitative data respectively. Results: According to the results, medical coding ward had poorer performance than other wards (2.37 out of 3). The problems of Health Information Management Department of Valiassr hospital were categorized in five main categories including organizational, human resources, financial, infrastructural and hardware and knowledge and professional categories. Also, scan of main sheets was the most important performed activity. Conclusion: With regard to the results of the present study, it is suggested that policy makers pay special attention to Health Information Management. 122 131 2017/05/142017/05/102017/06/192017/07/232017/07/15 1396/4/24 2017/09/62017/07/22017/09/22017/09/182017/09/2 1396/6/11 رضا محمدی Reza Mohammadi دکتری روانشناسی نظامی، مرکز تحقیقات کاربردی، معاونت بهداشت امداد و درمان نیروی انتظامی جمهوری اسلامی ایران، تهران، ایران. ایران rezamohammadi.psy@gmail.com مینا لازم Mina Lazem دانشجوی دکتری مدیریت اطلاعات سلامت، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع‎رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران. ایران minaalazem@gmail.com سارا قلنه Sara Ghalane دانشجوی کارشناسی ارشد فناوری اطلاعات سلامت، گروه مدیریت و فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی شهید بهشتی، تهران، ایران. ایران saraghalane@yahoo.com نسیم اصلانی Nasim Aslani دانشجوی دکتری مدیریت اطلاعات سلامت، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع‎رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران. ایران aslaninasim@yahoo.com علی گراوند Ali Garavand دانشجوی دکتری مدیریت اطلاعات سلامت، گروه مدیریت و فناوری اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی شهید بهشتی، تهران، ایران. ایران virya67@yahoo.com Performance Health information management Medical records عملکرد مدیریت اطلاعات سلامت پرونده پزشکی 1. Francis CM, De Souza MC. Hospital Administration. 3th ed. New Delhi: Jaypee Brothers Medical Publishers; 2000. ##2. Skurka MF. Organization of Medical Record Department in Hospital. 2th ed. American Hospital Association Publishing Ins; 1988.##3. Ahmadi M, Asadi F, Jalalifard B, Sadoughi F. Health Information Management (managing a strategic resource). Tehran: Vajehpardaz; 2004. Persian##4. Bergmann MM, Byers T, Freedman DS, Mokdad A. Validity of self-reported diagnoses leading to hospitalization: a comparison of self-reports with hospital records in a prospective study of American adults. Am J Epidemiol 1998;147(10):969-77.##5. Murphy BJ. Principles of good medical record documentation. J Med Pract Manage 2001;16(5):258-60. ##6. World Health Organization (WHO). Medical records manual: a guide for developing countries. Geneva: WHO; 2006.##7. Babaee A, Salavati F, Tavakkoli N, Tavakkoli R, Raiisi M, Golmohammadi F, et al. Comparing the frequency of recording information in admission and summary sheets in educational, private and insured hospitals. Health Info Manage 2003;1(1): 10-4. Persian##8. Moghadasi HA, Sheykh Taheri A. Organizational structure of the health information management departments in hospitals: a new model for Iran. Payesh 2008; 7(2):129-40. Persian ##9. Ghazi-saidi M, Safdari R, Davarpanah MR. Health Information Management. Tehran: Mirmah; 2005. Persian##10. Deraye S. Comparative survey of organizations and procedures in medical record department in teaching hospitals of Tehran and presenting pattern [dissertation]. Tehran: Iran University of Medical Sciences; 1998. Persian##11. Raeissi P, Mohammadpour A. A study on medical records department performances in Qom,s University for Medical Sciences teaching hospitals. J Health Adm 2008; 11(31): 39-46. Persian##12. Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs 2008;62(1):107-15.##13. Strauss A, Corbin J. Basics of Qualitative Research: Grounded Theory Procedures and Techniques. 2th ed. Los Angeles, SAGE Publications, Inc; 1990. ##14. Patton MQ. Qualitative Research & Evaluation Methods. SAGE Publications, Inc; 1990.##15. Tabibi SJ, Rabiei R, Sadoghi F. performance evaluation of medical record department of specialized-teaching hospitals of Iran Medical Science University. Feyz 2004; 8(29): 57-65. Persian##16. Bouraghi H, Khodadadi M. Evaluation of performance of the medical records unit in educational-remedial centers at Hamadan University of Medical Sciences. Pajouhan Scientific Journal 2012; 11(1):28-33. Persian##17. Ahmadi M, Samadbeik M, Birjandi M. Survey of the medical records filing in the teaching hospitals of Lorestan Univercity of Medical sciences in 2009. Yafte 2009; 11(3):73-83. Persian##18. Azizi AA, Torabipour A, Safari S, Mohhamadi A, Kheirollahi J, Shojaei Baghini M. Assessment of Medical Records Department Performance in Kermanshah Educational Hospitals; 2007; Iran. J Health Adm 2009;12(37): 17-22. Persian##19. Hajavi A, Sarbaz M, Moradi N. Medical Records 1, 2. 4th ed. Tehran: Jahan Rayaneh; 2002. Persian##20. Zarei J, Nadri K, Jalalaldin- Salehi M, Mohammadi A, Vahedi barzoki A. Surveying the status of the maintenance of medical records of casualties of Iraq-Iran war in hospitals affiliated to Ahvaz Jundishapur University of Medical Sciences. Journal of Clinical Research in Paramedical Sciences 2016; 4(4):341-50. Persian##21. Mehraeen E, Raeissi P, Omid Kohan Shoori Z, Ahmadi P, Jani Iranadgan S, Saravani S. Review of Medical Records Documentation in Obstetrics and Gynecology Ward (Data Quantitative Analysis on Obstetrics and Gynecology Ward). Advancing in Nursing and Midwifery 2014; 24(87): 37-44. Persian## ##

طراحی سیستم تحویل دارو برای تنظیم فشارخون با کمک کنترلر فازی Designing a Drug Delivery System for Regulation of Blood Pressure Using Fuzzy Controller 1 1 مقدمه: با توجه به اهمیت فشارخون بالا، کنترل و ثابت نگه‌داشتن سطح فشارخون در حالت نرمال بسیار حائز اهمیت است. هدف از این پژوهش، طراحی سیستم برای کنترل و تنظیم فشارخون مؤثر شریانی با استفاده از داروی سدیم نیتروپروساید به کمک کنترلر فازی است. روش: پژوهش حاضر از نوع کاربردی است که به روش مقطعی انجام شد. برای انجام این پژوهش ابتدا مطالعات مرتبط در حوزه طراحی مدل‌های ریاضی برای تنظیم فشارخون بررسی شدند. سپس با بررسی نتایج این مطالعات، بهترین مدل انتخاب شده و تغییرات لازم بر روی آن اعمال گردید. در مرحله آخر کنترلر فازی برای تنظیم فشارخون طراحی گردید، تمام عملیات طراحی و اجرای سیستم فازی در محیط نرم‌افزار متلب نسخه 2010 انجام شد. نتایج: در مرحله اول با اعمال کنترلر فازی، خروجی تغییرات سریع و ناگهانی داشت؛ لذا با اعمال اصلاحات در جهت بهبود کنترلر، سیستم توانست فشارخون بیمار را در زمان حدود 3 دقیقه، کنترل و به حالت نرمال خود برگرداند؛ اما بازهم خروجی کنترلر دارای تغییرات سریعی بود، لذا با اعمال یک کنترلر فیلتر شده، نتایج نشان داد که فرکانس‌های بالا کنترل و در زمان حدود 1 دقیقه فشارخون، تنظیم و ثابت ماند. نتیجه گیری: با استفاده از کنترل فازی و استفاده از مدل‌های ریاضی معتبر، می‌توان فشارخون بیمار را تنظیم و ثابت نگه داشت. روش‌های کلاسیک در برخورد با سیستم‌های بیولوژیکی وابستگی شدید به پارامترهای مدل دارند؛ اما کنترل فازی در برخورد با عدم قطعیت، پاسخ و سرعت عمل قابل قبولی را ارائه می‌دهد. 2 Introduction: According to the importance of hypertension, the control and stabilization of blood pressure level is very important. The purpose of this study was to design a system for controlling and regulating arterial blood pressure by using sodium nitroprusside through a fuzzy controller. Methods: This is an applied study done through cross-sectional method. At first, related studies in the field of designing mathematical models for regulation of blood pressure were investigated. Then, by surveying the results of these studies, the best model was selected and necessary changes were made. In the final phase, the fuzzy controller was designed for blood pressure regulation. All processes of designing and implementation of Fuzzy system were performed in 2010 version of MatLab software. Results: In the first phase, the fuzzy controller caused rapid and sudden changes in output. Therefore, by modifying the controller to improve the system, the system was able to control the blood pressure after about 3 minutes and returned it to the normal level. But, again, the output of the controller had quick changes. So, by applying a filtered controller, high frequencies were controlled and blood pressure remained constant within about 1 minute. Conclusion: By using fuzzy control and valid mathematical models, blood pressure can be controlled and stabilized. Classical methods in dealing with biological systems are highly dependent on model parameters. But, fuzzy control, in facing uncertainty, provides an acceptable response with an acceptable speed. 132 141 2017/05/142017/05/102017/06/192017/07/232017/07/152017/07/5 1396/4/14 2017/09/62017/07/22017/09/22017/09/182017/09/22017/09/11 1396/6/20 ملیکا بابایی Melika Babaei کارشناس ارشد انفورماتیک پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی مشهد، مشهد، ایران. ایران babaeim5@mums.ac.ir حسام کریم Hesam Karim دانشجوی دکتری تخصصی انفورماتیک پزشکی، گروه مدیریت اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی تهران، تهران، ایران ایران karim.hesam@gmail.com شراره رستم نیاکان Sharareh Rostam Niakan دکتری تخصصی انفورماتیک پزشکی، استادیار، گروه مدیریت اطلاعات سلامت، دانشکده پیراپزشکی، دانشگاه علوم پزشکی تهران، تهران، ایران. ایران Niakan2@gmail.com Blood pressure Fuzzy controller Sodium Nitroproside Medical Informatics فشارخون کنترلر فازی سدیم نیترو پروساید انفورماتیک پزشکی 1. Saugel B, Dueck R, Wagner JY. Measurement of blood pressure. Best Pract Res Clin Anaesthesiol 2014;28(4):309-22.##2. World Health Organization (WHO). Raised blood pressure 2017 [cited 2017 Jun 19]. Available from: http://www.who.int/gho/ncd/risk_factors/blood_pressure_prevalence_text/en/.##3. Zheng YL, Ding XR, Poon CCY, Lo BP, Zhang H, Zhou XL, et al. Unobtrusive Sensing and Wearable Devices for Health Informatics. IEEE Transactions on Biomedical Engineering 2014;61(5):1538-54.##4. Barro S, Marín R. Fuzzy Logic in Medicine. Springer-Verlag Berlin Heidelberg: Physica-Verlag; 2002.##5. Mirza M, GholamHosseini H, Harrison MJ. A fuzzy logic-based system for anaesthesia monitoring. Annual International Conference of the IEEE Engineering in Medicine and Biology; 2010 Aug- Sep 31-4; Buenos Aires, Argentina: IEEE; 2010.##6. Baig MM, Gholamhosseini H, Harrison MJ. Fuzzy logic based smart anaesthesia monitoring system in the operation theatre. WSEAS Transactions on Circuits and Systems 2012;11(1):21-32.##7. Nguyen HT, Prasad NR, Walker CL, Walker EA. A First Course in Fuzzy and Neural Control. 1th ed. United Stattes of America: Chapman and Hall/CRC; 2002. ##8. Isaka S, Sebald A. An adaptive fuzzy controller for blood pressure regulation. Engineering in Medicine and Biology Society. Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society; 1989 Nov 9-12; Seattle, WA, USA, USA: IEEE; 1989.##9. Zheng H, Zhu K, Zhang D. Design of an adaptive drug delivery control system. Control and Automation. 2005 Jun 26-29; Budapest, Hungary: IEEE; 2005. ##10. Feng J, Bo Q, Kuanyi Z, editors. Implementation of Drug Delivery system for blood pressure regulation. 9th International Conference on Control, Automation, Robotics and Vision; 2006 Dec 5-8; Singapore: IEEE; 2006.##11. Huang J, Held C, Roy R, editors. Drug infusion for control of blood pressure during anesthesia. Proceedings of the 2000 American Control Conference. ACC; 2000 Jun 28-30; Chicago, IL, USA, USA: IEEE; 2000.##12. Er MJ, Gao Y. Adaptive control strategy for blood pressure regulation using a fuzzy neural network. Systems, Man and Cybernetics. IEEE International Conference on; 2003 Oct 8; Washington, DC, USA: IEEE; 2003.##13. Polycarpou MM, Conway JY. Indirect adaptive nonlinear control of drug delivery systems. IEEE Transactions on Automatic Control 1998;43(6):849-56.##14. Isaka S, Sebald A, Karimi A, Smith N, Quinn M. On the design and performance evaluation of adaptive fuzzy controllers. Proceedings of the 27th IEEE Conference on Decision and Control; 1988 Dec 7-9; Austin, TX, USA, USA: IEEE; 1988.##15. Slate JB. Model-based design of a controller for infusing sodium nitroprusside during postsurgical hypertension [dissertation]. Madison: University of Wisconsin; 1980. ##16. Sebald A, Schnurer G, Parti M, Smith N, Quinn M. A dynamic empirical model of the human response to sodium nitroprusside during cardiac surgery. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 1988 Nov 4-7; New Orleans, LA, USA, USA: IEEE; 1988.##17. He WG, Kaufman H, Roy R. Multiple Model Adaptive Control Procedure for Blood Pressure Control. IEEE Transactions on Biomedical Engineering 1986; 33(1):10-9.##18. Ying H, Mceachern M, Eddleman DW, Sheppard LC. Fuzzy Control of Mean Arterial Pressure in Postsurgical Patients with Sodium Nitroprusside Infusion. IEEE Transactions on Biomedical Engineering 1992;39(10):1060-70. ##19. Furutani E, Araki M, Sakamoto T, Maetani S. Blood pressure control during surgical operations. IEEE Trans Biomed Eng 1995;42(10):999-1006.##20. Slate J, Sheppard L, Rideout V, Blackstone E. A Model for Design of a Blood Pressure Controller for Hypertensive Patients. IFAC Proceedings Volumes 1979;12(8):867-74.##21. Djam X, Kimbi Y. Fuzzy Expert System for the Management of Hypertension. The Pacific Journal of Science and Technology 2011;12(1):390-402. ##22. Cavalcanti AL, Maitelli AL. Design of an Intelligent Adaptive Drug Delivery System for Arterial Pressure Control. Wseas Transactions on Systems and Control 2015;10: 704-12.##23. Krulevitch P, Wilk R, Kraft U, Savage D, Foley N, Glencross J, et al. Drug delivery management systems and methods. Google Patents; 2013.##24. Sheppard LC. Computer control of the infusion of vasoactive drugs. Annals of Biomedical Engineering 1980;8(4-6):431-44.##25. Widrow B. Adaptive Model Control Applied to Real-Time Blood-Pressure Regulation. In: Fu KS, editor. Pattern Recognition and Machine Learning: Proceedings of the Japan—US Seminar on the Learning Process in Control Systems, held in Nagoya, Japan 1970 Aug18–20; Boston, MA: Springer US; 1971. p. 310-24.##26. Arnsparger JM, McInnis BC, Glover JR, Normann NA. Adaptive control of blood pressure. IEEE Trans Biomed Eng 1983;30(3):168-76.##27. Koivo AJ. Automatic continuous-time blood pressure control in dogs by means of hypotensive drug injection. IEEE Trans Biomed Eng 1980;27(10):574-81.##28. Masuzawa T, Fukui Y. The control system for physiological system—Optimal control of blood pressure by using vaso-active drugs. Journal of the Japan Society for Precision Engineering 1989;55(2):406-11.##29. Abdelwahab W, Frishman W, Landau A. Management of hypertensive urgencies and emergencies. J Clin Pharmacol 1995;35(8):747-62.##30. Kaplan NM. Management of hypertensive emergencies. The Lancet 1994;344(8933):1335-8.##31. Vaughan CJ, Delanty N. Hypertensive emergencies. The Lancet 2000;356(9227):411-7.#### ##

طبقه‌بندی سیگنال‌های مغزی تصور حرکت دست چپ و راست در سامانه‌های واسط مغز و رایانه با استفاده از انتخاب ویژگی به کمک الگوریتم‌های فرا ‌ابتکاری Classification of L/R Hand Motor Imagery in Brain Computer Interfaces Using Feature Selection by Metaheuristic Algorithms 1 1 مقدمه: بازشناسی فعالیت‌های مختلف حسی- حرکتی در سامانه‌های واسط مغز و رایانه با مباحث بازشناسی الگو در ارتباط است. یکی از مسائل مهم در طراحی یک سامانه مؤثر واسط مغز و رایانه، چگونگی کاهش تعداد ویژگی‌های استخراج شده از سیگنال‌های مغزی است. استفاده از الگوریتم‌های انتخاب ویژگی یکی از مهم‌ترین مراحل در زمینه بازشناسی الگو می‌باشد. کاهش تعداد ویژگی‌ها می‌تواند در بهبود دقت و کارایی طبقه‌بند‌ها و در نتیجه کاهش هزینه‌ها مؤثر واقع شود. روش: در این مقاله انتخاب ویژگی با استفاده از دو الگوریتم جستجوی گرانشی باینری بهبود یافته و بهینه‌ساز جمعیت مورچگان باینری پیشرفته بر روی مجموعه داده‌های مربوط به سیگنال‌های مغزی 9 فرد سالم جهت تفکیک تصور حرکت دست چپ و راست، صورت گرفت. ویژگی‌ها در 6 زیر باند مختلف استخراج شده‌اند. دو طبقه‌بند ماشین بردار پشتیبان و k نزدیک‌ترین همسایه با استفاده از ویژگی‌های انتخاب شده بر روی نمونه‌ها اعمال شد. دادهها در محیط متلب و توسط جعبه ابزار EEGLAB پردازش شده است. نتایج: نرخ طبقه‌بندی در سیستم پیشنهادی بالای 80 درصد است. با استفاده از روش‌های انتخاب ویژگی، باندهای فرکانسی و ویژگی های مؤثر جهت طبقه‌بندی حرکت دست چپ و راست استخراج شده‌‌اند. نتیجه‌گیری: نتایج نشان دهنده‌ بهبود نتایج پس از اعمال الگوریتم جستجوی گرانشی باینری بهبود یافته و طبقه‌بند نزدیک‌ترین همسایه می‌باشد. 2 Introduction: Pattern recognition field is necessary for the recognition of different sensorimotor tasks in Brain Computer Interface systems. Reducing the number of features is an important step in Brain Computer Interface systems and it can improve the accuracy and efficiency of the classification and reduce the costs. Methods: In this paper, features selection was performed through using Improved Binary Gravitational search algorithm and Advanced Binary Ant Colony Optimization on data related to brain signals of nine normal subjects for imagination of left and right hand movements. Features were extracted from six different frequency bands. Two classifiers including support vector machine and k- nearest neighbor were applied to separate the classes. Data were processed by EEGLAB toolbox and through matlab software. Results: The classification rate of the proposed method is 84.21%. Using feature selection methods, effective frequency bands and features for left and right hand movement classification were extracted. Conclusion: The results show the improvement in the classification rate by using Improved Binary Gravitational search algorithm and nearest neighbor classification. 142 153 2017/05/142017/05/102017/06/192017/07/232017/07/152017/07/52017/05/21 1396/2/31 2017/09/62017/07/22017/09/22017/09/182017/09/22017/09/112017/09/10 1396/6/19 منصوره نکوئی Mansoureh Nekoei ارشناس ارشد برق، بخش مهندسی برق، دانشکده فنی و مهندسی، دانشگاه شهید باهنر کرمان، کرمان، ایران. ایران m.nakoei92@gmail.com حسین نظام ابادی پور Hossein Nezamabadi-pour دکترای برق، استاد بخش مهندسی برق، دانشکده فنی و مهندسی، دانشگاه شهید باهنر کرمان، کرمان، ایران. ایران nezam@uk.ac.ir عصمت راشدی Esmat Rashedi دکترای برق، استادیار گروه مخابرات، دانشکده برق و کامپیوتر، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران. ایران rashedi_es@yahoo.com Algorithm Advanced Binary Optimization Support Vector Machine الگوریتم گرانشی باینری بهبود یافته ماشین بردار پشتیبان 1. Alomari MH, Samaha A, AlKamha K. Automated classification of l/r hand movement eeg signals using advanced feature extraction and machine learning. International Journal of Advanced Computer Science and Applications 2013; 4(6): 207-12.##2. Zhang X, Wang YX, LI YN, Zhang JJ. An Approach for Pattern Recognition of EEG Applied in Prosthetic Hand Drive. Systems, Cybernetics and Informatics. 2010; 9(6): 51-6.##3. Rejer I. Genetic Algorithms in EEG Feature Selection for the Classification of Movements of the Left and Right Hand. In: Burduk R, Jackowski K, Kurzynski M, Wozniak M, Zolnierek A, editors. Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Heidelberg: Springer International Publishing; 2013. p. 579-89.##4. Peterson DA, Knight JN, Kirby MJ, Anderson CW, Thaut MH. Feature selection and blind source separation in an EEG-based brain-computer interface. Journal on Applied Signal Processing 2005;19: 3128-40.##5. Jochumsen M, Niazi IK, Dremstrup K, Kamavuako EN. 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شناسه‌زدایی پرونده الکترونیک سلامت با استفاده از الگوریتم‌های یادگیری ماشین: یک مرور نظام‌مند De-identification of Electronic Health Records Using Machine Learning Algorithms 1 1 مقدمه: پرونده الکترونیک سلامت حاوی اطلاعات بالینی زیادی است که برای فعالیت‌هایی چون پایش بهداشت عمومی، بهبود کیفیت و تحقیقات مورد استفاده قرار می‌گیرد. همچنین پرونده الکترونیک سلامت شامل اطلاعات سلامت قابل شناسایی است و همین موضوع اشتراک و استفاده ثانویه از پرونده‌ها را محدود می‌کند. شناسه‌زدایی یکی از رایج‌ترین روش‌های حفظ محرمانگی اطلاعات بیماران است. این مقاله مروری نظام‌مند بر تحقیقات اخیر می‌باشد، که به حذف تمامی شناسه‌ها از پرونده الکترونیک سلامت با استفاده از انواع روش‌های شناسه‌زدایی مبتنی بر یادگیری ماشین پرداخته‌اند. روش: این مقاله به صورت مروری نظام‌مند در بازه زمانی 2016 - 2006 در پایگاه‌های PubMed و Science direct انجام شد. مقالات با استفاده از چک‌لیست CASP و سپس توسط دو ارزیاب به‌طور مستقل بررسی و ارزشیابی شدند. در نهایت 12 مقاله با معیارهای ورود مطالعه همخوانی داشتند. نتایج: مقالات منتخب بر اساس روش و منابع دانش مورد استفاده، انواع شناسه‌ها، نوع اسناد بالینی، چالش‌ها و نتایج حاصل بررسی شده‌اند. نتایج نشان داد که در زمان انتشار داده‌های بالینی برای اهداف ثانویه شناسه‌زدایی مبتنی بر یادگیری ماشین راهکاری مناسب برای حفظ حریم خصوصی بیماران است. همچنین ترکیب الگوریتم‌های یادگیری ماشین و روش‌هایی چون تطابق الگو و عبارات منظم می‌تواند نیاز به داده آموزش را کاهش دهد. نتیجه‌گیری: در پرونده‌های پزشکی اطلاعات شناسایی زیادی وجود دارد. این مطالعه نشان داد که روش‌های شناسه‌زدایی مبتنی بر یادگیری ماشین می‌توانند به طرز چشمگیری خطر افشای این اطلاعات را کاهش دهند. 2 Introduction: Electronic Health Record (EHR) contains valuable clinical information that can be useful for activities such as public health surveillance, quality improvement, and research. However, EHRs often contain identifiable health information that their presence limits the use of the records for sharing and secondary usages. De-identification is one of the common methods for protecting the confidentiality of patient information. This systematic review has focused on recently published studies on the usage of de-identification methods based on Machine Learning (ML) approaches for removing all identifiable information from electronic health records. Methods: A systematic review was performed in electronic databases like PubMed and ScienceDirect between 2006 and 2016. Studies were assessed for adherence to the CASP checklists and reviewed independently by two investigators. Finally, 12 articles were matched with inclusion criteria. Results: The selected studies have been discussed in terms of used methods and knowledge resources, types of identifiers detected, types of clinical documents, challenges and achieved results. The results showed that ML-based de-identification is a widely invoked approach to protect patient privacy when disclosing clinical data for secondary purposes, such as research. Also, the combination of the ML algorithms and some techniques such as pattern matching and regular expression matching could decrease need to train data. Conclusion: There is a lot of identifiable information in medical records. This study showed ML- based de-identification methods can intensively reduce the disclosure risk of information. 154 167 2017/05/142017/05/102017/06/192017/07/232017/07/152017/07/52017/05/212017/07/17 1396/4/26 2017/09/62017/07/22017/09/22017/09/182017/09/22017/09/112017/09/102017/09/2 1396/6/11 مصطفی لنگری زاده Mostafa Langarizadeh دانشجوی دکترای انفورماتیک پزشکی، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع‌ رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران langarizadeh2001@yahoo.com اعظم اروجی Azam Orooji دانشجوی دکترای انفورماتیک پزشکی، گروه مدیریت اطلاعات سلامت، دانشکده مدیریت و اطلاع‌رسانی پزشکی، دانشگاه علوم پزشکی ایران، تهران، ایران ایران orooji.a@tak.iums.ac.ir Confidentiality Privacy De-identification Machine Learning محرمانگی حریم خصوصی شناسه‌زدایی یادگیری ماشین پرونده الکترونیک سلامت 1. Faghihi M, Memarzadeh G, Astane H. Protection of patient privacy; A prerequisite for electronic health development. Journal of Medical Ethics 2010;4(2):163-88. Persian##2. 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