Systematic review of approaches to use of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions.

Journal: Journal of biomedical informatics
Published Date: Feb 18, 2021

Abstract

BACKGROUND: Despite a large body of literature investigating how the environment influences health outcomes, most published work to date includes only a limited subset of the rich clinical and environmental data that is available and does not address how these data might best be used to predict clinical risk or expected impact of clinical interventions.

Authors

  • Katie Wilkinson
    Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, United States; School of Medicine, University of Missouri, Columbia, MO 65212, United States. Electronic address: wilkinsonka@health.missouri.edu.
  • Lincoln Sheets
    Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, United States; School of Medicine, University of Missouri, Columbia, MO 65212, United States.
  • Dale Fitch
    Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, United States; School of Social Work, University of Missouri, Columbia, MO 65212, United States.
  • Lori Popejoy
    Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, United States; School of Nursing, University of Missouri, Columbia, MO 65212, United States.

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