Machine learning approaches for predicting high cost high need patient expenditures in health care.

Journal: Biomedical engineering online
PMID: 30458798

Abstract

BACKGROUND: This paper studies the temporal consistency of health care expenditures in a large state Medicaid program. Predictive machine learning models were used to forecast the expenditures, especially for the high-cost, high-need (HCHN) patients.

Authors

  • Chengliang Yang
    BCIG Environmental Remediation Co., Ltd, Tianjin 300042, China.
  • Chris Delcher
    Department of Health Outcomes and Policy, University of Florida, Clinical and Translational Research Building, 2004 Mowry Road, P.O. Box 100219, Gainesville, FL, 32610, USA.
  • Elizabeth Shenkman
    Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA.
  • Sanjay Ranka
    Dept. of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, USA, ximen14@ufl.edu, anand@cise.ufl.edu, ranka@cise.ufl.edu.

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