Optimal intensive care outcome prediction over time using machine learning.

Journal: PloS one
PMID: 30427913

Abstract

BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission.

Authors

  • Christopher Meiring
    Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom.
  • Abhishek Dixit
    Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom.
  • Steve Harris
    Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom.
  • Niall S MacCallum
    Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom.
  • David A Brealey
    Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom.
  • Peter J Watkinson
    Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom.
  • Andrew Jones
    Department of Intensive Care, Guy's and St. Thomas' NHS Foundation Trust, St. Thomas' Hospital, Westminster Bridge Road, Lambeth, London.
  • Simon Ashworth
    Centre for Perioperative Medicine and Critical Care Research, Imperial College Healthcare NHS Trust, Praed St., London, United Kingdom.
  • Richard Beale
    Department of Intensive Care, Guy's and St. Thomas' NHS Foundation Trust, St. Thomas' Hospital, Westminster Bridge Road, Lambeth, London.
  • Stephen J Brett
    Centre for Perioperative Medicine and Critical Care Research, Imperial College Healthcare NHS Trust, Praed St., London, United Kingdom.
  • Mervyn Singer
    Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom.
  • Ari Ercole
    Division of Anaesthesia, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.

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