Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.

Journal: The Lancet. Digital health
Published Date: Apr 1, 2020

Abstract

BACKGROUND: Many mortality prediction models have been developed for patients in intensive care units (ICUs); most are based on data available at ICU admission. We investigated whether machine learning methods using analyses of time-series data improved mortality prognostication for patients in the ICU by providing real-time predictions of 90-day mortality. In addition, we examined to what extent such a dynamic model could be made interpretable by quantifying and visualising the features that drive the predictions at different timepoints.

Authors

  • Hans-Christian Thorsen-Meyer
    Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Intensive Care, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
  • Annelaura B Nielsen
    Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  • Anna P Nielsen
    Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  • Benjamin Skov Kaas-Hansen
    Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Clinical Pharmacology Unit, Zealand University Hospital, Roskilde, Denmark.
  • Palle Toft
    Department of Anesthesiology and Intensive Care, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Jens Schierbeck
    Department of Anesthesiology and Intensive Care, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Thomas Strøm
    Department of Anesthesiology and Intensive Care, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Piotr J Chmura
    Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  • Marc Heimann
    Centre for IT, Medical Technology and Telephony Services, Capital Region of Denmark, Copenhagen, Denmark.
  • Lars Dybdahl
    Daintel, Lyngby, Denmark.
  • Lasse Spangsege
    Daintel, Lyngby, Denmark.
  • Patrick Hulsen
    Daintel, Lyngby, Denmark.
  • Kirstine Belling
    Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark.
  • Søren Brunak
    NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
  • Anders Perner
    Department of Intensive Care, Copenhagen University Hospital-Rigshospitalet, DK-2100 Copenhagen, Denmark.

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