Machine-Learning Algorithms to Code Public Health Spending Accounts.

Journal: Public health reports (Washington, D.C. : 1974)
Published Date: Mar 31, 2017

Abstract

OBJECTIVES: Government public health expenditure data sets require time- and labor-intensive manipulation to summarize results that public health policy makers can use. Our objective was to compare the performances of machine-learning algorithms with manual classification of public health expenditures to determine if machines could provide a faster, cheaper alternative to manual classification.

Authors

  • Eoghan S Brady
    1 Department of Population, Family and Reproductive Health, School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
  • Jonathon P Leider
    2 Department of Health Policy and Management, School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
  • Beth A Resnick
    2 Department of Health Policy and Management, School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
  • Y Natalia Alfonso
    1 Department of Population, Family and Reproductive Health, School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
  • David Bishai
    1 Department of Population, Family and Reproductive Health, School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

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