Abstract
Social networks and Internet of things are two paradigms when integrated a new paradigm Internet of Everything is established that has its impact on revolutionizing various fields such as engineering, industry and healthcare. Social networks became nowadays of the most important web services on which people heavily rely, thus became a major source for information extraction for rational decision making considering individuals as social or socio sensors. Furthermore, people using sensors especially biological sensors enabled the use of internet of things technology in building intelligent healthcare systems. One of the challenges facing the design of such systems is the design of an intelligent recommender system that is able to deal with such big data. For that, this paper proposes a framework to develop an enhanced intelligent expert advisor-based health monitoring and disease awareness system. The proposed framework enables the researchers to design advisory systems that are able to observe physiological signals through the use of different bio sensors and integrate it with historical medical data together with � the massive data collected from social networks to provide accurate alerts and recommendations for many ailments inspected. The proposed Framework is designed to facilitate generic, dynamic and scalable process of integrating different types of social networks and bio sensors.
Highlights
Online social networks dominated the majority of web services during the last decade, and researchers became more interested in relying on social networks for information extraction
The proposed Framework is inspired from the fact that in the expected future, different technological revolutions in the fields of social networks, Internet of things will have its great impact on revolutionizing healthcare
The proposed Framework serves as a promising framework in the Internet of Everything era for serving the healthcare field, the framework is a new trend if compared with other recent frameworks serving the healthcare field under the internet of things umbrella, new algorithms for data integration and merging for information extraction from both socio sensors and biosensors, followed by filtration and classification techniques for feeding the knowledge base of the advisory intelligent expert system taking into consideration the medical historical data will be a step to serve future applications in healthcare as it could be altered to serve different diseases
Summary
Online social networks dominated the majority of web services during the last decade, and researchers became more interested in relying on social networks for information extraction. Adding human experiences supported by historical healthcare data ensure bringing personalized healthcare and intelligent monitoring systems to a challenging level To attain this intelligence level, it is essential to be able to analyze patient data in real time, and taking advantages of the social networks’ big data of interest to provide quality of experience to the medical data and turn it into reactive awareness to patients. This is the major objective of this research. The system’s knowledgebase will be built on medical healthcare facts and user experiences captured from information extracted by analyzing social networks’ big data using big data analytics algorithms, in addition to relevant features extracted from processing sensors’ data using real time processing techniques and algorithms
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Online and Biomedical Engineering (iJOE)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
