| Literature DB >> 29726357 |
Todd R McNutt1, Stanley H Benedict2, Daniel A Low3, Kevin Moore4, Ilya Shpitser5, Wei Jiang6, Pranav Lakshminarayanan6, Zhi Cheng6, Peijin Han6, Xuan Hui7, Minoru Nakatsugawa8, Junghoon Lee6, Joseph A Moore6, Scott P Robertson6, Veeraj Shah6, Russ Taylor5, Harry Quon6, John Wong6, Theodore DeWeese6.
Abstract
Big clinical data analytics as a primary component of precision medicine is discussed, identifying where these emerging tools fit in the spectrum of genomics and radiomics research. A learning health system (LHS) is conceptualized that uses clinically acquired data with machine learning to advance the initiatives of precision medicine. The LHS is comprehensive and can be used for clinical decision support, discovery, and hypothesis derivation. These developing uses can positively impact the ultimate management and therapeutic course for patients. The conceptual model for each use of clinical data, however, is different, and an overview of the implications is discussed. With advancements in technologies and culture to improve the efficiency, accuracy, and breadth of measurements of the patient condition, the concept of an LHS may be realized in precision radiation therapy.Entities:
Mesh:
Year: 2018 PMID: 29726357 DOI: 10.1016/j.ijrobp.2018.02.028
Source DB: PubMed Journal: Int J Radiat Oncol Biol Phys ISSN: 0360-3016 Impact factor: 7.038