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

Big Data Decision Support Systems, Clinical Forecasting Humans Intensive Care Units Machine Learning Precision Medicine

Journal

Anaesthesia, critical care & pain medicine
ISSN: 2352-5568
Titre abrégé: Anaesth Crit Care Pain Med
Pays: France
ID NLM: 101652401

Informations de publication

Date de publication:
08 2019
Historique:
received: 31 05 2018
revised: 31 07 2018
accepted: 04 09 2018
pubmed: 20 10 2018
medline: 31 7 2020
entrez: 20 10 2018
Statut: ppublish

Résumé

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.

Identifiants

DOI: 10.1016/j.accpm.2018.09.008 PMID: 30339893
pubmed: 30339893
pii: S2352-5568(18)30216-9
doi: 10.1016/j.accpm.2018.09.008
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

377-384

Subventions

Organisme : NIGMS NIH HHS
ID : R01 GM117622
Pays : United States
Organisme : NINR NIH HHS
ID : R01 NR013912
Pays : United States

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2018 Société française d’anesthésie et de réanimation (Sfar). Published by Elsevier Masson SAS. All rights reserved.

Auteurs

Romain Pirracchio (R)

Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA; Department of anesthesia and perioperative medicine, university of California San Francisco, CA, USA; Service d'anesthésie-réanimation, hôpital Européen Georges-Pompidou, université Paris Descartes, Sorbonne Paris Cite, 75015 Paris, France; Service de biostatistique et informatique médicale, hôpital Saint-Louis, Inserm UMR-1153, université Paris Diderot, Sorbonne Paris Cite, 75010 Paris, France. Electronic address: romain.pirracchio@aphp.fr.

Mitchell J Cohen (MJ)

Department of surgery, university of Colorado Denver, Colorado, USA.

Ivana Malenica (I)

Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA.

Jonathan Cohen (J)

Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA.

Antoine Chambaz (A)

MAP5 (UMR CNRS 8145), université Paris Descartes, 75006 Paris, France.

Maxime Cannesson (M)

Department of anesthesiology and perioperative medicine, university of California Los Angeles, CA, USA; Department of bioengineering, university of California Irvine, CA, USA.

Christine Lee (C)

Department of bioengineering, university of California Irvine, CA, USA.

Matthieu Resche-Rigon (M)

Service de biostatistique et informatique médicale, hôpital Saint-Louis, Inserm UMR-1153, université Paris Diderot, Sorbonne Paris Cite, 75010 Paris, France.

Alan Hubbard (A)

Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Informatique › Informatique médicale › Applications de l'informatique médicale › Systèmes d'information › Mégadonnées › Big data and targeted machine learning in...
questionsmedicales.fr › Sciences de l'information › Informatique › Informatique médicale › Applications de l'informatique médicale › Systèmes d'information › Systèmes d'aide à la décision clinique › Big data and targeted machine learning in...
questionsmedicales.fr › Sciences sociales › Prévision › Big data and targeted machine learning in...
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questionsmedicales.fr › Établissements, main d'oeuvre et services de soins de santé › Établissements de santé › Unités hospitalières › Unités de soins intensifs › Big data and targeted machine learning in...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Big data and targeted machine learning in...
questionsmedicales.fr › Professions de santé › Médecine › Médecine clinique › Médecine de précision › Big data and targeted machine learning in...