Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence.

Journal: BMC medical informatics and decision making
Published Date: Aug 30, 2021

Abstract

BACKGROUND: Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospital stay. In this study, we use electronic health records and administrative data to examine the contributing factors to PI development using artificial intelligence (AI).

Authors

  • Christine Anderson
    School of Nursing, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Zerihun Bekele
    Statistics Online Computational Resource (SOCR), University of Michigan, Ann Arbor, MI, 48109, USA.
  • Yongkai Qiu
    Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA.
  • Dana Tschannen
    School of Nursing, University of Michigan, Ann Arbor, MI, 48109, USA. djvs@med.umich.edu.
  • Ivo D Dinov
    Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA; Statistics Online Computational Resource, Department of Health Behavior and Biological, University of Michigan, Ann Arbor, MI, USA.

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