Big data and targeted machine learning in action to assist medical decision in the ICU.

Journal: Anaesthesia, critical care & pain medicine
Published Date: Oct 16, 2018

Abstract

Historically, personalised medicine has been synonymous with pharmacogenomics and oncology. We argue for a new framework for personalised medicine analytics that capitalises on more detailed patient-level data and leverages recent advances in causal inference and machine learning tailored towards decision support applicable to critically ill patients. We discuss how advances in data technology and statistics are providing new opportunities for asking more targeted questions regarding patient treatment, and how this can be applied in the intensive care unit to better predict patient-centred outcomes, help in the discovery of new treatment regimens associated with improved outcomes, and ultimately how these rules can be learned in real-time for the patient.

Authors

  • Romain Pirracchio
  • Mitchell J Cohen
    Division of General Surgery, Department of Surgery, School of Medicine, University of California San Francisco, San Francisco, California, United States of America.
  • Ivana Malenica
    Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA.
  • Jonathan Cohen
    Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA.
  • Antoine Chambaz
    MAP5 (UMR CNRS 8145), université Paris Descartes, 75006 Paris, France.
  • Maxime Cannesson
    Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
  • Christine Lee
    Department of Psychiatry and Behavioral Sciences, University of Washington, USA.
  • Matthieu Resche-Rigon
    Service de biostatistique et informatique médicale, hôpital Saint-Louis, Inserm UMR-1153, université Paris Diderot, Sorbonne Paris Cite, 75010 Paris, France.
  • Alan Hubbard
    Division of Biostatistics, University of California, Berkeley, California, United States of America.

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