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Using Big Data Analytics to Advance Precision Radiation Oncology.
McNutt, Todd R; Benedict, Stanley H; Low, Daniel A; Moore, Kevin; Shpitser, Ilya; Jiang, Wei; Lakshminarayanan, Pranav; Cheng, Zhi; Han, Peijin; Hui, Xuan; Nakatsugawa, Minoru; Lee, Junghoon; Moore, Joseph A; Robertson, Scott P; Shah, Veeraj; Taylor, Russ; Quon, Harry; Wong, John; DeWeese, Theodore.
Afiliação
  • McNutt TR; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland. Electronic address: tmcnutt1@jhmi.edu.
  • Benedict SH; Department of Radiation Oncology, University of California, Davis, Sacramento, California.
  • Low DA; Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, California.
  • Moore K; Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California.
  • Shpitser I; Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
  • Jiang W; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Lakshminarayanan P; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Cheng Z; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Han P; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Hui X; Department of Public Health Sciences, University of Chicago, Chicago, Illinois.
  • Nakatsugawa M; Canon Medical Systems Corporation.
  • Lee J; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Moore JA; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Robertson SP; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Shah V; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Taylor R; Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
  • Quon H; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Wong J; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • DeWeese T; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
Int J Radiat Oncol Biol Phys ; 101(2): 285-291, 2018 06 01.
Article em En | MEDLINE | ID: mdl-29726357
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radioterapia (Especialidade) / Sistemas de Apoio a Decisões Clínicas / Medicina de Precisão / Aprendizado de Máquina / Big Data Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radioterapia (Especialidade) / Sistemas de Apoio a Decisões Clínicas / Medicina de Precisão / Aprendizado de Máquina / Big Data Idioma: En Ano de publicação: 2018 Tipo de documento: Article