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...

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Detalles Bibliográficos
Autores principales: Sanders, Jason C., Showalter, Timothy N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5954037/
https://www.ncbi.nlm.nih.gov/pubmed/29868477
http://dx.doi.org/10.3389/fonc.2018.00155
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author Sanders, Jason C.
Showalter, Timothy N.
author_facet Sanders, Jason C.
Showalter, Timothy N.
author_sort Sanders, Jason C.
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description 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.
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spelling pubmed-59540372018-06-04 How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology Sanders, Jason C. Showalter, Timothy N. Front Oncol 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. Frontiers Media S.A. 2018-05-09 /pmc/articles/PMC5954037/ /pubmed/29868477 http://dx.doi.org/10.3389/fonc.2018.00155 Text en Copyright © 2018 Sanders and Showalter. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Sanders, Jason C.
Showalter, Timothy N.
How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology
title How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology
title_full How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology
title_fullStr How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology
title_full_unstemmed How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology
title_short How Big Data, Comparative Effectiveness Research, and Rapid-Learning Health-Care Systems Can Transform Patient Care in Radiation Oncology
title_sort how big data, comparative effectiveness research, and rapid-learning health-care systems can transform patient care in radiation oncology
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5954037/
https://www.ncbi.nlm.nih.gov/pubmed/29868477
http://dx.doi.org/10.3389/fonc.2018.00155
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