Big data and targeted machine learning in action to assist medical decision in the ICU - 24/07/19

Doi : 10.1016/j.accpm.2018.09.008

Romain Pirracchio ^a,^b,^c,^d,^⁎ , Mitchell J Cohen ^e, Ivana Malenica ^a, Jonathan Cohen ^a, Antoine Chambaz ^f, Maxime Cannesson ^g,^h, Christine Lee ^h, Matthieu Resche-Rigon ^d, Alan Hubbard ^a
the ACTERREA Research Group
^a Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA
^b Department of anesthesia and perioperative medicine, university of California San Francisco, CA, USA
^c Service d’anesthésie-réanimation, hôpital Européen Georges-Pompidou, université Paris Descartes, Sorbonne Paris Cite, 75015 Paris, France
^d Service de biostatistique et informatique médicale, hôpital Saint-Louis, Inserm UMR-1153, université Paris Diderot, Sorbonne Paris Cite, 75010 Paris, France
^e Department of surgery, university of Colorado Denver, Colorado, USA
^f MAP5 (UMR CNRS 8145), université Paris Descartes, 75006 Paris, France
^g Department of anesthesiology and perioperative medicine, university of California Los Angeles, CA, USA
^h Department of bioengineering, university of California Irvine, CA, USA

^⁎Corresponding author at: Service d’anesthesie-réanimation, hôpital Européen Georges-Pompidou, 20, rue Leblanc, 75015 Paris, France.Service d’anesthesie-réanimationhôpital Européen Georges-Pompidou20, rue LeblancParis75015France

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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.

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Plan

Introduction

The past and the present: examples of big data and machine learning in biomedicine

The data: big data at the bedside in the ICU

Use of big data for personalized medicine in the ICU

Machine learning for predictive analytics and decision support in the ICU

The future: going from using fixed data to learning “on the fly”

Statistical methods for online learning

Methods for assisting clinical decision

Conclusion and perspectives

Ethical statement

Disclosure of interest

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Vol 38 - N° 4

P. 377-384 - août 2019 Retour au numéro

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Big data and targeted machine learning in action to assist medical decision in the ICU - 24/07/19

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