Difference between revisions of "Template:Article of the week"

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(Updated article of the week text.)
(Updated article of the week text.)
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig5 eSilva Sensors2018 18-8.jpg|240px]]</div>
'''"[[Journal:How big data, comparative effectiveness research, and rapid-learning health care systems can transform patient care in radiation oncology|How big data, comparative effectiveness research, and rapid-learning health care systems can transform patient care in radiation oncology]]"'''
'''"[[Journal:Wireless positioning in IoT: A look at current and future trends|Wireless positioning in IoT: A look at current and future trends]]"'''


Connectivity solutions for the [[internet of things]] (IoT) aim to support the needs imposed by several applications or use cases across multiple sectors, such as logistics, [[Agriculture industry|agriculture]], asset management, or smart lighting. Each of these applications has its own challenges to solve, such as dealing with large or massive networks, low and ultra-low latency requirements, long battery life requirements (i.e., more than ten years operation on battery), continuously monitoring of the location of certain nodes, security, and authentication. Hence, a part of picking a connectivity solution for a certain application depends on how well its features solve the specific needs of the end application. One key feature that we see as a need for future IoT networks is the ability to provide location-based [[information]] for large-scale IoT applications. ('''[[Journal:Wireless positioning in IoT: A look at current and future trends|Full article...]]''')<br />
Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning health care system (RLHCS) to accelerate discovery and to help turn the dream of fully personalized medicine into a reality. We synthesize recent advances in [[genomics]] with trends in big data to provide a forward-looking perspective on the potential of new advances to usher in an era of personalized radiation therapy, with emphases on the power of RLHCS to accelerate discovery and the future of individualized radiation treatment planning. ('''[[Journal:How big data, comparative effectiveness research, and rapid-learning health care systems can transform patient care in radiation oncology|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'':
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Revision as of 19:20, 7 October 2018

"How big data, comparative effectiveness research, and rapid-learning health care systems can transform patient care in radiation oncology"

Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning health care system (RLHCS) to accelerate discovery and to help turn the dream of fully personalized medicine into a reality. We synthesize recent advances in genomics with trends in big data to provide a forward-looking perspective on the potential of new advances to usher in an era of personalized radiation therapy, with emphases on the power of RLHCS to accelerate discovery and the future of individualized radiation treatment planning. (Full article...)

Recently featured:

▪ Wireless positioning in IoT: A look at current and future trends
▪ Password compliance for PACS work stations: Implications for emergency-driven medical environments
▪ Data science as an innovation challenge: From big data to value proposition
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