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صفحه اصلی
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هفتمین همايش ملی پيشرفت های معماری سازمانی
Leveraging Big Data Intelligence for Electronic Health Data Collection and Mining in Smart Innovative Cities: Approaches and Applications
نویسندگان :
S. Mohammadhosain Zanjani (دانشگاه آزاد اسلامی واحد نجف آباد) , S. Mohammadali Zanjani (دانشگاه آزاد اسلامی واحد نجف آباد) , Hossein Shahinzadeh (دانشگاه صنعتی امیرکبیر) , Majid Moazzami (دانشگاه آزاد اسلامی واحد نجف آباد) , Farshad Ebrahimi (University of Houston) , Nasim Nourmahnad (دانشگاه آزاد اسلامی واحد نجف آباد)
کلمات کلیدی :
Big data،Electronic health records،Health care،Smart city،Information security،Machine learning،Data mining،Data collection،Artificial intelligence،Internet of things
چکیده :
In recent years, the Internet of Things (IoT) and the development of smart cities have brought about significant advancements in various fields, including medicine. The integration of IoT devices and smart city infrastructure has transformed domains like e-government, e-learning, and e-health. In particular, the field of e-health, which combines medical informatics, public health, and information technology, has witnessed extensive research to analyze the vast amounts of data generated by these connected systems. This data, often referred to as big data, poses challenges in terms of storage, analysis, visualization, and processing due to its complexity and diversity. The emergence of big data analytics has revolutionized management practices, including healthcare, and offers promising opportunities for progress. By leveraging data analysis techniques and optimizing the architecture of healthcare systems within the context of IoT and smart cities, numerous benefits can be achieved. These include cost reduction, early detection of epidemics, and improved quality of life for individuals. This article aims to provide a comprehensive overview of fundamental concepts, architectural considerations, and challenges in utilizing big data for e-health and healthcare within the context of IoT and smart cities. By fostering a deeper understanding of data-driven approaches for better healthcare outcomes, it seeks to contribute to further advancements in this field.
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