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The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period

Acute kidney injury (AKI) is a common medical problem in hospitalised patients worldwide that may result in negative physiological, social and economic consequences. Amongst patients admitted to ICU with AKI, over 40% have had either elective or emergency surgery prior to admission. Predicting outco...

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Detalles Bibliográficos
Autores principales: Kelly, Barry J., Chevarria, Julio, O’Sullivan, Barry, Shorten, George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672488/
https://www.ncbi.nlm.nih.gov/pubmed/34906249
http://dx.doi.org/10.1186/s13741-021-00219-y
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author Kelly, Barry J.
Chevarria, Julio
O’Sullivan, Barry
Shorten, George
author_facet Kelly, Barry J.
Chevarria, Julio
O’Sullivan, Barry
Shorten, George
author_sort Kelly, Barry J.
collection PubMed
description Acute kidney injury (AKI) is a common medical problem in hospitalised patients worldwide that may result in negative physiological, social and economic consequences. Amongst patients admitted to ICU with AKI, over 40% have had either elective or emergency surgery prior to admission. Predicting outcomes after AKI is difficult and the decision on whom to initiate RRT with a goal of renal recovery or predict a long-term survival benefit still poses a challenge for acute care physicians. With the increasing use of electronic healthcare records, artificial intelligence may allow postoperative AKI prognostication and aid clinical management. Patients will benefit if the data can be readily accessed andregulatory, ethical and human factors challenges can be overcome.
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spelling pubmed-86724882021-12-15 The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period Kelly, Barry J. Chevarria, Julio O’Sullivan, Barry Shorten, George Perioper Med (Lond) Editorial Acute kidney injury (AKI) is a common medical problem in hospitalised patients worldwide that may result in negative physiological, social and economic consequences. Amongst patients admitted to ICU with AKI, over 40% have had either elective or emergency surgery prior to admission. Predicting outcomes after AKI is difficult and the decision on whom to initiate RRT with a goal of renal recovery or predict a long-term survival benefit still poses a challenge for acute care physicians. With the increasing use of electronic healthcare records, artificial intelligence may allow postoperative AKI prognostication and aid clinical management. Patients will benefit if the data can be readily accessed andregulatory, ethical and human factors challenges can be overcome. BioMed Central 2021-12-15 /pmc/articles/PMC8672488/ /pubmed/34906249 http://dx.doi.org/10.1186/s13741-021-00219-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Editorial
Kelly, Barry J.
Chevarria, Julio
O’Sullivan, Barry
Shorten, George
The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period
title The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period
title_full The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period
title_fullStr The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period
title_full_unstemmed The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period
title_short The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period
title_sort potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672488/
https://www.ncbi.nlm.nih.gov/pubmed/34906249
http://dx.doi.org/10.1186/s13741-021-00219-y
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