TY - JOUR
T1 - Big data for nutrition research in pediatric oncology
T2 - Current state and ramework for advancement
AU - Phillips, Charles A.
AU - Pollock, Brad H.
N1 - Publisher Copyright:
© 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Recognition and treatment of malnutrition in pediatric oncology patients is crucial because it is associated with increased morbidity and mortality. Nutrition-relevant data collected from cancer clinical trials and nutrition-specific studies are insufficient to drive high-impact nutrition research without augmentation from additional data sources. To date, clinical big data resources are underused for nutrition research in pediatric oncology. Health-care big data can be broadly subclassified into three clinical data categories: administrative, electronic health record (including clinical data research networks and learning health systems), and mobile health. Along with -omics data, each has unique applications and limitations. We summarize the potential use of clinical big data to drive pediatric oncology nutrition research and identify key scientific gaps. A framework for advancement of big data utilization for pediatric oncology nutrition research is presented and focuses on transdisciplinary teams, data interoperability, validated cohort curation, data repurposing, and mobile health applications.
AB - Recognition and treatment of malnutrition in pediatric oncology patients is crucial because it is associated with increased morbidity and mortality. Nutrition-relevant data collected from cancer clinical trials and nutrition-specific studies are insufficient to drive high-impact nutrition research without augmentation from additional data sources. To date, clinical big data resources are underused for nutrition research in pediatric oncology. Health-care big data can be broadly subclassified into three clinical data categories: administrative, electronic health record (including clinical data research networks and learning health systems), and mobile health. Along with -omics data, each has unique applications and limitations. We summarize the potential use of clinical big data to drive pediatric oncology nutrition research and identify key scientific gaps. A framework for advancement of big data utilization for pediatric oncology nutrition research is presented and focuses on transdisciplinary teams, data interoperability, validated cohort curation, data repurposing, and mobile health applications.
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U2 - 10.1093/jncimonographs/lgz019
DO - 10.1093/jncimonographs/lgz019
M3 - Article
C2 - 31532530
AN - SCOPUS:85072277018
SN - 1052-6773
VL - 2019
SP - 127
EP - 131
JO - NCI Monographs
JF - NCI Monographs
IS - 54
ER -