Physiological machine learning models for prediction of sepsis in hospitalized adults: An integrative review.

Journal: Intensive & critical care nursing
Published Date: Apr 17, 2021

Abstract

BACKGROUND: Diagnosing sepsis remains challenging. Data compiled from continuous monitoring and electronic health records allow for new opportunities to compute predictions based on machine learning techniques. There has been a lack of consensus identifying best practices for model development and validation towards early identification of sepsis.

Authors

  • Sherry L Kausch
    University of Virginia School of Nursing, Charlottesville, VA, USA; University of Virginia Center for Advanced Medical Analytics, Charlottesville, VA, USA; School of Data Science, University of Virginia, Charlottesville, VA, USA. Electronic address: slk7s@virginia.edu.
  • J Randall Moorman
    University of Virginia School of Medicine, Department of Internal Medicine, Division of Cardiovascular Diseases, Charlottesville, VA, USA; University of Virginia Center for Advanced Medical Analytics, Charlottesville, VA, USA. Electronic address: rm3h@virginia.edu.
  • Douglas E Lake
    University of Virginia School of Medicine, Department of Internal Medicine, Division of Cardiovascular Diseases, Charlottesville, VA, USA; University of Virginia Center for Advanced Medical Analytics, Charlottesville, VA, USA. Electronic address: del2k@virginia.edu.
  • Jessica Keim-Malpass
    Artera, Santa Barbara, CA.

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