Predicting pressure injury using nursing assessment phenotypes and machine learning methods.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Mar 18, 2021

Abstract

OBJECTIVE: Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical methods, limiting their accuracy. In this paper, we describe the development of machine learning-based predictive models, using phenotypes derived from nurse-entered direct patient assessment data.

Authors

  • Wenyu Song
    Division of General Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA.
  • Min-Jeoung Kang
    Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA. mkang6@bwh.harvard.edu.
  • Linying Zhang
    Department of Biomedical Informatics, Columbia University, New York, New York, USA.
  • Wonkyung Jung
    School of Nursing, University of Washington, Seattle, Washington, USA.
  • Jiyoun Song
    School of Nursing, Columbia University, New York, New York, USA.
  • David W Bates
    Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
  • Patricia C Dykes
    Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.

Keywords

External Resources

Popular Topics
Recent Journals