Machine Learning-Based Mortality Prediction of Patients at Risk During Hospital Admission.

Journal: Journal of patient safety
Published Date: Jan 13, 2022

Abstract

OBJECTIVES: The ability to predict in-hospital mortality from data available at hospital admission would identify patients at risk and thereby assist hospital-wide patient safety initiatives. Our aim was to use modern machine learning tools to predict in-hospital mortality from standardized data sets available at hospital admission.

Authors

  • Kevin M Trentino
    Data and Digital Innovation, East Metropolitan Health Service, Perth, Australia.
  • Karin Schwarzbauer
    Institute for Machine Learning, Johannes Kepler University, Linz, Austria.
  • Andreas Mitterecker
    Institute for Machine Learning, Johannes Kepler University, Linz, Austria.
  • Axel Hofmann
    Department of Anesthesiology and Critical Care Medicine, University and University Hospital, Zürich, Switzerland.
  • Adam Lloyd
    Data and Digital Innovation, East Metropolitan Health Service, Perth, Australia.
  • Michael F Leahy
    Department of Haematology, PathWest Laboratory Medicine, Royal Perth Hospital, Perth, Australia.
  • Thomas Tschoellitsch
    Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH and Johannes Kepler University, Linz, Austria.
  • Carl Böck
    Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH and Johannes Kepler University, Linz, Austria.
  • Sepp Hochreiter
    Institute for Machine Learning Johannes Kepler University Linz Austria.
  • Jens Meier
    Department of Anesthesiology and Intensive Care Medicine, Faculty of Medicine of the Kepler University Linz, Krankenhausstraße 9, 4020 Linz, Austria. Electronic address: jens.meier@gmail.com.

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