Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach.

Journal: International journal of medical informatics
Published Date:

Abstract

BACKGROUND: Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48h after ICU admission.

Authors

  • Aya Awad
    University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK.
  • Mohamed Bader-El-Den
    University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK. Electronic address: mohamed.bader@port.ac.uk.
  • James McNicholas
    University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK; Queen Alexandra Hospital, Portsmouth Hospitals NHS Trust, UK.
  • Jim Briggs
    University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth PO1 3HE, UK.