تشخيص أمراض الحج الشائعة عن طريق برامج الوسائط المتعددة التفاعلية
محتوى المقالة الرئيسي
الملخص
في هذه الدراسة، نحاول تقديم خدمة الرعاية الصحية للحجاج. تصف هذه الدراسة كيف يمكن استخدام مناهج الوسائط المتعددة في جعل الحجاج على علم بالأمراض الشائعة الموجودة في المملكة العربية السعودية أثناء موسم الحج. كما سيتم استخدام البرامج التعليمية للوسائط المتعددة في توفير بعض المعلومات حول أعراض هذه الأمراض، وكيف يمكن علاج كل منها. يحتوي البرنامج التعليمي للوسائط المتعددة على تمثيل افتراضي للمستشفى، وبعض مقاطع الفيديو للحالات الفعلية للمرضى، وأنشطة التعلم الأصيلة التي تهدف إلى تعزيز الكفاءات الصحية أثناء الحج. تم فحص المناهج الدراسية لدراسة الطريقة التي يتم بها تطبيق عناصر المناهج الدراسية في التعلم في الوقت الحقيقي. أكثر من ذلك، في هذا البحث، يتم تقديم مناقشة حول أخطر الأمراض التي قد تحدث خلال موسم الحج. إن استخدام دورة الوسائط المتعددة قادر على توفير المعلومات بشكل فعال وفعال للحجاج حول هذه الأمراض. تؤدي هذه التقنية هذه المهمة باستخدام المعرفة المتراكمة من التجارب السابقة، لا سيما في مجال تشخيص الأمراض والطب والعلاج. تم إنشاء المناهج الدراسية باستخدام أداة تأليف تُعرف باسم مدرب ToolBook لتزويد الحجاج بخدمة عالية الجودة.
تفاصيل المقالة
هذا العمل مرخص بموجب Creative Commons Attribution 4.0 International License.
كيفية الاقتباس
المراجع
Karampourian A, Khorasani-Zavareh Da, Ghomian Z. Communicable Diseases Pattern in Religious Mass Gatherings: A Systematic Review. J Clin Diagn Res. 2019 Feb 1;13(2).
Yezli S, Van der Linden M, Booy R, AlOtaibi B. Pneumococcal disease during Hajj and Umrah: Research agenda for evidence-based vaccination policy for these events. Travel Med. Infect. Dis. 2019 May 1;29:8-15.
Khan NA, Ishag AM, Ahmad MS, El-Sayed FM, Bachal ZA, Abbas TG. Pattern of medical diseases and determinants of prognosis of hospitalization during 2005 Muslim pilgrimage (Hajj) in a tertiary care hospital. A prospective cohort study. Saudi Med J. 2006 Sep 1;27(9):1373.
AboEl-Magd GH, Alkhotani N, Elsawy A. The prevalence and pattern of pneumonia among Hajj pilgrims: a study of two successive Hajj seasons. Egypt J Chest Dis Tuberc. 2020 Apr 1;69(2):407.
Aldossari M, Aljoudi A, Celentano D. Health issues in the Hajj pilgrimage: a literature review. East Mediterr Health J. 2019;25(10):744-53.
Memish ZA, Assiri A, Turkestani A, Yezli S, Al Masri M, Charrel R, et al. Mass gathering and globalization of respiratory pathogens during the 2013 Hajj. Clin Microbiol Infect. 2015 Jun 1;21(6):571-e1.
Mardhatillah M. Specific Treatment of Elderly Pilgrims on Hajj According to the Hadith; The Approach of Mukhtalif Ahadis. Al-Ihkam: J Hukum Sosial. 2019 Jun 30;14(1):98-121.
Mohammed MA, Ghani MK, Arunkumar NA, Mostafa SA, Abdullah MK, Burhanuddin MA. Trainable model for segmenting and identifying Nasopharyngeal carcinoma. Comput Electr Eng. 2018 Oct 1;71:372-87.
Arunkumar N, Mohammed MA, Mostafa SA, Ibrahim DA, Rodrigues JJ, de Albuquerque VH. Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurr. Comput. Pract. Exp. 2020 Jan 10;32(1):e4962.
Abd Ghani MK, Mohammed MA, Arunkumar N, Mostafa SA, Ibrahim DA, Abdullah MK, et al. Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Comput Appl. 2020 Feb 1;32(3):625-38.
Mohammed MA, Ghani MK, Arunkumar NA, Hamed RI, Mostafa SA, Abdullah MK, et al. Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network. J Supercomput. 2018 Sep 6: 76, 1086–1104.
Çakiroğlu Ü, Gökoğlu S. Development of fire safety behavioral skills via virtual reality. Comput Educ. 2019 May 1;133:56-68.
Egbert N, Thye J, Hackl WO, Müller-Staub M, Ammenwerth E, Hübner U. Competencies for nursing in a digital world. Methodology, results, and use of the DACH-recommendations for nursing informatics core competency areas in Austria Germany, and Switzerland. Inform Health Soc Care. 2019 Oct 2;44(4):351-75.
Molenda M. In search of the elusive ADDIE model. J of Perform improvement. 2003 May;42(5):34-7.
Mohammed MA, Ghani MK, Hamed RI, Ibrahim DA. Analysis of an electronic methods for nasopharyngeal carcinoma: Prevalence, diagnosis, challenges and technologies. J. Comput. Sci. 2017 Jul 1;21:241-54.
Mohammed MA, Al-Khateeb B, Rashid AN, Ibrahim DA, Ghani MK, Mostafa SA. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Comput Electr Eng. 2018 Aug 1;70:871-82.
Mostafa SA, Mustapha A, Mohammed MA, Hamed RI, Arunkumar N, Ghani MK, et al. Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease. Cogn. Syst. Res. 2019 May 1;54:90-9.
Muda Z, Mohamed RE. Adaptive user interface design in multimedia courseware. In2006 2nd International Conference on Information & Communication Technologies 2006 Apr 24 (Vol. 1, pp. 196-199). IEEE.
Nur Fathyah AR. Animated Pedagogical Agent Courseware On Photosynthesis For School Kids. 2008.
Turan J. Multimedia Teleeducation Courseware: Adafox–––Modelling Digital And Analogue Fiber Optical Networks. 2007.
Lee TR. Teaching biostatistics to medical personnel with computer based supplement. Training Researchers in the Use of Statistics. 2001:139-45.
Molenda M. The ADDIE model. Encyclopedia of Educational Technology, ABC-CLIO. 2003.
Molenda M. In search of the elusive ADDIE model. J of Perform improvement. 2003 May;42(5):34-7.
Gulbahar Y, Adanır GA. Emerging Instructional Design and Strategies for Online Courses. InHandbook of Research on Developing Engaging Online Courses 2020 (pp. 94-115). IGI Global.
Heo M, Toomey N. Learning with multimedia: The effects of gender, type of multimedia learning resources, and spatial ability. Comput Educ. 2020 Mar 1;146:103747.
Fiorella L, Pilegard C. Learner-generated explanations: effects on restudying and learning from a multimedia lesson. Educ. Psychol. 2020 Apr 20:1-8.
Hoch E, Scheiter K, Schüler A. Implementation Intentions Related to Self-Regulatory Processes Do Not Enhance Learning in a Multimedia Environment. Front. Psychol. 2020 Jan 22;11:46.
Karimah L, Haryono H, Ahmadi F. The Development of Bolokuncoro Interactive Learning Multimedia for Language Literacy of Children Aged 5-6 Years Old. Indones. j. prim. educ. 2020;9(2):144-51.
Obaid OI, Mohammed MA, Ghani MK, Mostafa A, Taha F. Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast CancerInt. Int J Res Eng Technol. 2018;7(4.36):160-6.
Arunkumar N, Mohammed MA, Abd Ghani MK, Ibrahim DA, Abdulhay E, Ramirez-Gonzalez G, de Albuquerque VH. K-means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Comput.. 2019 Oct 1;23(19):9083-96.
Mostafa SA, Mustapha A, Khaleefah SH, Ahmad MS, Mohammed MA. Evaluating the performance of three classification methods in diagnosis of Parkinson’s disease. In International Conference on Soft Comput & Data Mining 2018 Feb 6 (pp. 43-52). Springer, Cham.
Ghani MK, Mohamed MA, Mostafa SA, Mustapha A, Aman H, Jaber MM. The Design of Flexible Telemedicine Framework for Healthcare Big Data. Int J Res Eng Techno. 2018;7(3.20):461-8.
Abd Ghani MK, Noma NG, Mohammed MA, Abdulkareem KH, Garcia-Zapirain B, Maashi MS, Mostafa SA. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability. 2021 Jan;13(10):5406.
Mohammed M, Al-Sharify T, Kolivand H. Real-Time Cloth Simulation on Virtual Human Character Using Enhanced Position Based Dynamic Framework Technique. Baghdad Sci. J. 2020 Dec 1;17(4):1294-.
Salah, H.A. and Ahmed, A.S., 2021. Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System. Baghdad Sci. J, 18(3), pp.0593-0593.