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

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text.)
Line 1: Line 1:
'''"[[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]]"'''
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Evans Informatics2017 4-4.png|240px]]</div>
'''"[[Journal:A data quality strategy to enable FAIR, programmatic access across large, diverse data collections for high performance data analysis|A data quality strategy to enable FAIR, programmatic access across large, diverse data collections for high performance data analysis]]"'''


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 />
To ensure seamless, programmatic access to data for high-performance computing (HPC) and [[Data analysis|analysis]] across multiple research domains, it is vital to have a methodology for standardization of both data and services. At the Australian National Computational Infrastructure (NCI) we have developed a data quality strategy (DQS) that currently provides processes for: (1) consistency of data structures needed for a high-performance data (HPD) platform; (2) [[quality control]] (QC) through compliance with recognized community standards; (3) benchmarking cases of operational performance tests; and (4) [[quality assurance]] (QA) of data through demonstrated functionality and performance across common platforms, tools, and services. ('''[[Journal:A data quality strategy to enable FAIR, programmatic access across large, diverse data collections for high performance data analysis|Full article...]]''')<br />
<br />
<br />
''Recently featured'':
''Recently featured'':
: ▪ [[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]]
: ▪ [[Journal:Wireless positioning in IoT: A look at current and future trends|Wireless positioning in IoT: A look at current and future trends]]
: ▪ [[Journal:Password compliance for PACS work stations: Implications for emergency-driven medical environments|Password compliance for PACS work stations: Implications for emergency-driven medical environments]]
: ▪ [[Journal:Password compliance for PACS work stations: Implications for emergency-driven medical environments|Password compliance for PACS work stations: Implications for emergency-driven medical environments]]
: ▪ [[Journal:Data science as an innovation challenge: From big data to value proposition|Data science as an innovation challenge: From big data to value proposition]]

Revision as of 18:45, 15 October 2018

Fig1 Evans Informatics2017 4-4.png

"A data quality strategy to enable FAIR, programmatic access across large, diverse data collections for high performance data analysis"

To ensure seamless, programmatic access to data for high-performance computing (HPC) and analysis across multiple research domains, it is vital to have a methodology for standardization of both data and services. At the Australian National Computational Infrastructure (NCI) we have developed a data quality strategy (DQS) that currently provides processes for: (1) consistency of data structures needed for a high-performance data (HPD) platform; (2) quality control (QC) through compliance with recognized community standards; (3) benchmarking cases of operational performance tests; and (4) quality assurance (QA) of data through demonstrated functionality and performance across common platforms, tools, and services. (Full article...)

Recently featured:

How big data, comparative effectiveness research, and rapid-learning health care systems can transform patient care in radiation oncology
Wireless positioning in IoT: A look at current and future trends
Password compliance for PACS work stations: Implications for emergency-driven medical environments