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Big data and machine learning in radiation oncology: state of the art and future prospects

Highlights • Computing power and data storage costs are continuously decreasing • Electronic Health Records can now be used to create comprehensive phenotypic profiles • Genomics can be correlated to these phenotypic profiles to better understand treatment response and toxicity • Combining EHR and G... Full description

Journal Title: Cancer letters 2016, Vol.382 (1), p.110-117
Main Author: Bibault, Jean-Emmanuel, MD MSc
Other Authors: Giraud, Philippe, MD, PhD , Burgun, Anita, MD PhD
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: Ireland: Elsevier Ireland Ltd
ID: ISSN: 0304-3835
Link: https://www.ncbi.nlm.nih.gov/pubmed/27241666
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title: Big data and machine learning in radiation oncology: state of the art and future prospects
format: Article
creator:
  • Bibault, Jean-Emmanuel, MD MSc
  • Giraud, Philippe, MD, PhD
  • Burgun, Anita, MD PhD
subjects:
  • Algorithms
  • Big Data
  • Biomedical Research - methods
  • Biomedical Research - statistics & numerical data
  • Chemotherapy
  • Classification
  • Data Interpretation, Statistical
  • Data Mining - methods
  • Data Mining - statistics & numerical data
  • Databases, Factual - statistics & numerical data
  • Decision Support Techniques
  • Drug dosages
  • Electronic health records
  • Electronic Health Records - statistics & numerical data
  • Genomics
  • Hematology, Oncology and Palliative Medicine
  • Humans
  • Immunotherapy
  • Machine Learning
  • Medical research
  • Medicine
  • Neoplasms - pathology
  • Neoplasms - radiotherapy
  • Neural networks
  • Neural Networks (Computer)
  • Oncology
  • Patients
  • Predictive model
  • Quality
  • Radiation
  • Radiation Dosage
  • Radiation oncology
  • Radiation Oncology - methods
  • Radiation Oncology - statistics & numerical data
  • Radiation therapy
  • Radiotherapy - adverse effects
  • Radiotherapy Planning, Computer-Assisted
  • Risk Assessment
  • Risk Factors
  • Software
  • Support Vector Machine
  • Unsupervised Machine Learning
ispartof: Cancer letters, 2016, Vol.382 (1), p.110-117
description: Highlights • Computing power and data storage costs are continuously decreasing • Electronic Health Records can now be used to create comprehensive phenotypic profiles • Genomics can be correlated to these phenotypic profiles to better understand treatment response and toxicity • Combining EHR and Genomics through Machine Learning could generate high-quality evidence for precision medicine • These methods could be used to create a “learning health system” to predict the outcome of any treatment
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0304-3835
fulltext: fulltext
issn:
  • 0304-3835
  • 1872-7980
url: Link


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descriptionHighlights • Computing power and data storage costs are continuously decreasing • Electronic Health Records can now be used to create comprehensive phenotypic profiles • Genomics can be correlated to these phenotypic profiles to better understand treatment response and toxicity • Combining EHR and Genomics through Machine Learning could generate high-quality evidence for precision medicine • These methods could be used to create a “learning health system” to predict the outcome of any treatment
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subjectAlgorithms ; Big Data ; Biomedical Research - methods ; Biomedical Research - statistics & numerical data ; Chemotherapy ; Classification ; Data Interpretation, Statistical ; Data Mining - methods ; Data Mining - statistics & numerical data ; Databases, Factual - statistics & numerical data ; Decision Support Techniques ; Drug dosages ; Electronic health records ; Electronic Health Records - statistics & numerical data ; Genomics ; Hematology, Oncology and Palliative Medicine ; Humans ; Immunotherapy ; Machine Learning ; Medical research ; Medicine ; Neoplasms - pathology ; Neoplasms - radiotherapy ; Neural networks ; Neural Networks (Computer) ; Oncology ; Patients ; Predictive model ; Quality ; Radiation ; Radiation Dosage ; Radiation oncology ; Radiation Oncology - methods ; Radiation Oncology - statistics & numerical data ; Radiation therapy ; Radiotherapy - adverse effects ; Radiotherapy Planning, Computer-Assisted ; Risk Assessment ; Risk Factors ; Software ; Support Vector Machine ; Unsupervised Machine Learning
ispartofCancer letters, 2016, Vol.382 (1), p.110-117
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abstractHighlights • Computing power and data storage costs are continuously decreasing • Electronic Health Records can now be used to create comprehensive phenotypic profiles • Genomics can be correlated to these phenotypic profiles to better understand treatment response and toxicity • Combining EHR and Genomics through Machine Learning could generate high-quality evidence for precision medicine • These methods could be used to create a “learning health system” to predict the outcome of any treatment
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