دانلود رایگان مقاله انگلیسی اینترنت اشیا در بهداشت و درمان: یک بررسی اجمالی - الزویر 2016

abstract

Extensive research has been dedicated to the exploration of various technologies such as information technologies (IT) in complementing and strengthening existing healthcare services. In particular, the Internet of Things (IoT) has been widely applied to interconnect available medical resources and provide reliable, effective and smart healthcare service to the elderly and patients with a chronic illness. The aim of this paper is to summarize the applications of IoT in the healthcare industry and identify the intelligentization trend and directions of future research in this field. Based on a comprehensive literature review and the discussion of the achievements of the researchers, the advancement of IoT in healthcare systems have been examined from the perspectives of enabling technologies and methodologies, IoT-based smart devices and systems, and diverse applications of IoT in the healthcare industries. Finally, the challenges and prospects of the development of IoT based healthcare systems are discussed in detail.

7. Conclusion and future development

Conclusion can be made that the rapidly advancing information technologies and emerging IoT technology have provided great op- portunities for developing smart healthcare information systems. Nevertheless, challenges still exist in achieving secure and effec- tive tele-healthcare applications. Some identified areas for future  improvements are listed as follows:

(1) Self-learning and self-improvement. Facing the tremendous in- formation and great complexity, IoT itself cannot provide re- habilitation treatments or construct medical resources. Prompt and effective treatments must be made based on other two factors, quick diagnosis for patients, and creations of rehabil- itation treatments based on the diagnosis. Even with similar symptoms, the conditions of patients vary from one to an- other. All the factors have to be taken into account in or- der to generate an effective therapeutic regimen. A computer- aided tool relies merely on the data acquired by sensors and records of past similar cases, while self-learning methods can adaptively and intelligently diagnose and recommend the treat- ments. Some self-learning algorithms, such as Artificial Neu- ral Network (ANN), Genetic Algorithms (GA), Ant Colony Opti- mization (ACO), and Simulated Annealing (SA), can be applied to analyze data and mine knowledge. Besides, healthcare re- sources can be very dynamic due to reconfiguration, and pa- tients need to share the limited healthcare resources with the lowest cost and the highest efficiency. Topology- and ontology- based heuristic algorithms have demonstrated their power in finding optimal solutions for a large scale system.