On classifying sepsis heterogeneity in the ICU: insight using machine learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Mar 1, 2020

Abstract

OBJECTIVES: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR data.

Authors

  • Zina M Ibrahim
    Department of Biostatistics and Health Informatics, Institute of Psychiatry Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, United Kingdom.
  • Honghan Wu
    University College London, London, United Kingdom.
  • Ahmed Hamoud
    Department of Renal Medicine, East and North Hertfordshire NHS Trust, Stevenage, UK.
  • Lukas Stappen
    Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.
  • Richard J B Dobson
    Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, UK.
  • Andrea Agarossi
    Department of Anaesthesia and Intensive Care, Luigi Sacco Hospital, Milan, Italy.

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