• Todd R McNutt, Stanley H Benedict, Daniel A Low, Kevin Moore, Ilya Shpitser, Wei Jiang, Pranav Lakshminarayanan, Zhi Cheng, Peijin Han, Xuan Hui, Minoru Nakatsugawa, Junghoon Lee, Joseph A Moore, Scott P Robertson, Veeraj Shah, Russ Taylor, Harry Quon, John Wong, and Theodore DeWeese.
• Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland. Electronic address: tmcnutt1@jhmi.edu.
• Int. J. Radiat. Oncol. Biol. Phys. 2018 Jun 1; 101 (2): 285-291.

AbstractBig 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.Copyright © 2018 Elsevier Inc. All rights reserved.

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