OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality.

Journal: BMC medical informatics and decision making
Published Date: May 13, 2021

Abstract

BACKGROUND: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called "weights" or "subscores") into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved.

Authors

  • Yasser El-Manzalawy
    Systems and Computer Engineering, Al-Azhar University, Cairo, Egypt; College of Information Sciences, Penn State University, University Park, United States of America.
  • Mostafa Abbas
    Department of Imaging Science and Innovation, Geisinger Health System, Danville, PA, 17822, USA.
  • Ian Hoaglund
    College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, 16802, USA.
  • Alvaro Ulloa Cerna
    Department of Translational Data Science and Informatics, Geisinger, Danville, PA, 17822, USA.
  • Thomas B Morland
    Department of General Internal Medicine, Geisinger, Danville, PA, 17822, USA.
  • Christopher M Haggerty
    IT Data Science, NewYork-Presbyterian Hospital, New York, New York, USA.
  • Eric S Hall
    Department of Translational Data Science and Informatics, Geisinger, Danville, PA, 17822, USA.
  • Brandon K Fornwalt
    Department of Imaging Science and Innovation, Geisinger, Danville, Pennsylvania; Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky; Department of Radiology, Geisinger, Danville, Pennsylvania. Electronic address: bkf@gatech.edu.

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