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

被引：39

作者：

Pirracchio, Romain ^{[1
,2
,3
,4
]}

Cohen, Mitchell J. ^{[5
]}

Malenica, Ivana ^{[1
]}

Cohen, Jonathan ^{[1
]}

Chambaz, Antoine ^{[6
]}

Cannesson, Maxime ^{[7
,8
]}

Lee, Christine ^{[8
]}

Resche-Rigon, Matthieu ^{[4
]}

Hubbard, Alan ^{[1
]}

机构：

[1] Univ Calif Berkeley, Sch Publ Hlth, Div Biostat, Berkeley, CA 94720 USA

[2] Univ Calif San Francisco, Dept Anesthesia & Perioperat Med, San Francisco, CA 94143 USA

[3] Univ Paris 05, Serv Anesthesie Reanimat, Sorbonne Paris Cite, Hop Europeen Georges Pompidou, F-75015 Paris, France

[4] Univ Paris Diderot, Serv Biostat & Informat Med, Sorbonne Paris Cite, Hop St Louis,Inserm UMR 1153, F-75010 Paris, France

[5] Univ Colorado, Dept Surg, Denver, CO 80202 USA

[6] Univ Paris 05, MAP5 UMR CNRS 8145, F-75006 Paris, France

[7] Univ Calif Los Angeles, Dept Anesthesiol & Perioperat Med, Los Angeles, CA 90024 USA

[8] Univ Calif Irvine, Dept Bioengn, Irvine, CA 92717 USA

来源：

ANAESTHESIA CRITICAL CARE & PAIN MEDICINE | 2019年 / 38卷 / 04期

关键词：

PREDICTION; OUTCOMES; READMISSIONS; UNITS; ARDS; TIME;

D O I：

10.1016/j.accpm.2018.09.008

中图分类号：

R614 [麻醉学];

学科分类号：

100217 ;

摘要：

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. (C) 2018 Societe francaise d'anesthesie et de reanimation (Sfar). Published by Elsevier Masson SAS. All rights reserved.

引用

页码：377 / 384

页数：8

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