Literature DB >> 32763775

Prediction on critically ill patients: The role of "big data".

Lucas Bulgarelli1, Rodrigo Octávio Deliberato2, Alistair E W Johnson3.   

Abstract

Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simple ordinal severity of illness scores which could be tabulated manually by a human. With the improvements in computing power and proliferation of electronic medical records, entirely new approaches have become possible. Here we review the latest advances in outcome prediction, paying close attention to methods which are widely applicable and provide a high-level overview of the challenges the field currently faces.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Critical Care; Machine learning; Outcome prediction

Mesh:

Year:  2020        PMID: 32763775     DOI: 10.1016/j.jcrc.2020.07.017

Source DB:  PubMed          Journal:  J Crit Care        ISSN: 0883-9441            Impact factor:   3.425


  3 in total

1.  Validation of an Automatic Tagging System for Identifying Respiratory and Hemodynamic Deterioration Events in the Intensive Care Unit.

Authors:  Danielle Jeddah; Ofer Chen; Ari M Lipsky; Andrea Forgacs; Gershon Celniker; Craig M Lilly; Itai M Pessach
Journal:  Healthc Inform Res       Date:  2021-07-31

2.  Identification of acute respiratory distress syndrome subphenotypes de novo using routine clinical data: a retrospective analysis of ARDS clinical trials.

Authors:  Abhijit Duggal; Rachel Kast; Emily Van Ark; Lucas Bulgarelli; Matthew T Siuba; Jeff Osborn; Diego Ariel Rey; Fernando G Zampieri; Alexandre Biasi Cavalcanti; Israel Maia; Denise M Paisani; Ligia N Laranjeira; Ary Serpa Neto; Rodrigo Octávio Deliberato
Journal:  BMJ Open       Date:  2022-01-06       Impact factor: 2.692

3.  Prediction algorithm for ICU mortality and length of stay using machine learning.

Authors:  Shinya Iwase; Taka-Aki Nakada; Tadanaga Shimada; Takehiko Oami; Takashi Shimazui; Nozomi Takahashi; Jun Yamabe; Yasuo Yamao; Eiryo Kawakami
Journal:  Sci Rep       Date:  2022-07-28       Impact factor: 4.996

  3 in total

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