|
Tags: Manual revert Replaced |
| (85 intermediate revisions by the same user not shown) |
| Line 3: |
Line 3: |
| | type = notice | | | type = notice |
| | style = width: 960px; | | | style = width: 960px; |
| | text = This is my sandbox, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see [[User_talk:Shawndouglas|my discussion page]] instead.<p></p> | | | text = This is my primary sandbox page, where I play with features and test MediaWiki code. If you wish to leave a comment for me, please see [[User_talk:Shawndouglas|my discussion page]] instead.<p></p> |
| }} | | }} |
|
| |
|
| ==Sandbox begins below== | | ==Sandbox begins below== |
|
| |
| {{Infobox journal article
| |
| |name =
| |
| |image =
| |
| |alt = <!-- Alternative text for images -->
| |
| |caption =
| |
| |title_full = How big data, comparative effectiveness research, and rapid-learning healthcare<br />systems can transform patient care in radiation oncology
| |
| |journal = ''Frontiers in Oncology''
| |
| |authors = Sanders, Jason C.; Showalter, Timothy N.
| |
| |affiliations = University of Virginia School of Medicine
| |
| |contact = Email: tns3b@virginia.edu
| |
| |editors = Deng, Jun
| |
| |pub_year = 2018
| |
| |vol_iss = '''8'''
| |
| |pages = 155
| |
| |doi = [http://10.3389/fonc.2018.00155 10.3389/fonc.2018.00155]
| |
| |issn = 2234-943X
| |
| |license = [http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International]
| |
| |website = [https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/full https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/full]
| |
| |download = [https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/pdf https://www.frontiersin.org/articles/10.3389/fonc.2018.00155/pdf] (PDF)
| |
| }}
| |
| {{ombox
| |
| | type = content
| |
| | style = width: 500px;
| |
| | text = This article should not be considered complete until this message box has been removed. This is a work in progress.
| |
| }}
| |
| ==Introduction==
| |
| Big data and comparative effectiveness research methodologies can be applied within the framework of a rapid-learning healthcare 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.
| |
|
| |
| ==Comparative effectiveness research (CER) and big data==
| |
| The Committee on CER Prioritization was created by the Institute of Medicine in 2009. They defined CER as “a strategy that focuses on the practical comparison of two or more health intervention to discern what works best for which patients and populations.”<ref name="IoMInitial09">{{cite book |url=https://www.nap.edu/catalog/12648/initial-national-priorities-for-comparative-effectiveness-research |title=Initial National Priorities for Comparative Effectiveness Research |author=Institute of Medicine of the National Academies |publisher=National Academies Press |year=2009 |isbn=9780309138369}}</ref> In essence, the goal of CER is to identify "which treatment will work best, in which patient, under what circumstances.”<ref name="GreenfieldWelcome12">{{cite journal |title=Welcome to the Journal of Comparative Effectiveness Research |journal=Journal of Comparative Effectiveness Research |author=Greenfield, S.; Rich, E. |volume=1 |issue=1 |pages=1–3 |year=2012 |doi=10.2217/cer.11.13 |pmid=24237290}}</ref> Big data refers to data sets that are so large that they cannot be analyzed directly by individuals or traditional processing software. Big data analytics (BDA) is a growing field with a multitude of methods that is being utilized in various sectors from business to medicine.<ref name="SivarajahCritical17">{{cite journal |title=Critical analysis of Big Data challenges and analytical methods |journal=Journal of Business Research |author=Sivarajah, U.; Kamal, M.M.; Irani, Z.; Weerakkody, V. |volume=70 |pages=263–86 |year=2017 |doi=10.1016/j.jbusres.2016.08.001}}</ref> The advent of the [[electronic medical record]] (EMR) has resulted in the digitization of massive data sets of medical information, including clinic encounters, [[laboratory]] values, imaging data sets and reports, pathology reports, patient outcomes, and family history, as well as genomic and biological data, etc.
| |
|
| |
| To help with the [[Data analysis|analysis]] of big data, the [[National Institutes of Health]] (NIH) has created the Big Data to Knowledge (BD2K) program, which has invested over $200 million in grant awards to foster the development of methods and tools to analyze big data in biomedical research (4). Additionally, the BD2K program will move to make sure that biomedical big data is “findable, accessible, interoperable, and reusable” (FAIR).<ref name="MargolisTheNat14">{{cite journal |title=The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: Capitalizing on biomedical big data |journal=JAMIA |author=Margolis, R.; Derr, L.; Dunn, M. et al. |volume=21 |issue=6 |pages=957–8 |year=2014 |doi=10.1136/amiajnl-2014-002974 |pmid=25008006 |pmc=PMC4215061}}</ref> Over the past decade, CER methodologies have become increasingly prevalent in radiation oncology research, and there is much enthusiasm surrounding BDA.
| |
|
| |
| ==References==
| |
| {{Reflist|colwidth=30em}}
| |
|
| |
| ==Notes==
| |
| This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
| |
|
| |
| <!--Place all category tags here-->
| |
| [[Category:LIMSwiki journal articles (added in 2018)]]
| |
| [[Category:LIMSwiki journal articles (all)]]
| |
| [[Category:LIMSwiki journal articles on big data]]
| |
| [[Category:LIMSwiki journal articles on health informatics]]
| |
