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Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing

In a recent article in Pediatric Nephrology, Olivier Niel and colleagues applied an artificial intelligence algorithm to a clinical problem that continues to challenge experienced pediatric nephrologists: optimizing the target weight of children on dialysis. They compared blood pressure, antihyperte...

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Detalles Bibliográficos
Autores principales: Hayes, Wesley, Allinovi, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132900/
https://www.ncbi.nlm.nih.gov/pubmed/30003314
http://dx.doi.org/10.1007/s00467-018-4021-4
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author Hayes, Wesley
Allinovi, Marco
author_facet Hayes, Wesley
Allinovi, Marco
author_sort Hayes, Wesley
collection PubMed
description In a recent article in Pediatric Nephrology, Olivier Niel and colleagues applied an artificial intelligence algorithm to a clinical problem that continues to challenge experienced pediatric nephrologists: optimizing the target weight of children on dialysis. They compared blood pressure, antihypertensive medication and intradialytic symptoms in children whose target weight was prescribed firstly by a nephrologist, then subsequently using a machine learning algorithm. Improvements in all outcome measures are reported. Their innovative approach to tackling this important clinical problem appears promising. In this editorial, we discuss the strengths and weaknesses of their study and consider to what extent machine learning strategies are suited to optimizing pediatric dialysis outcomes.
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spelling pubmed-61329002018-09-13 Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing Hayes, Wesley Allinovi, Marco Pediatr Nephrol Editorial Commentary In a recent article in Pediatric Nephrology, Olivier Niel and colleagues applied an artificial intelligence algorithm to a clinical problem that continues to challenge experienced pediatric nephrologists: optimizing the target weight of children on dialysis. They compared blood pressure, antihypertensive medication and intradialytic symptoms in children whose target weight was prescribed firstly by a nephrologist, then subsequently using a machine learning algorithm. Improvements in all outcome measures are reported. Their innovative approach to tackling this important clinical problem appears promising. In this editorial, we discuss the strengths and weaknesses of their study and consider to what extent machine learning strategies are suited to optimizing pediatric dialysis outcomes. Springer Berlin Heidelberg 2018-07-12 2018 /pmc/articles/PMC6132900/ /pubmed/30003314 http://dx.doi.org/10.1007/s00467-018-4021-4 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Editorial Commentary
Hayes, Wesley
Allinovi, Marco
Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing
title Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing
title_full Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing
title_fullStr Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing
title_full_unstemmed Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing
title_short Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing
title_sort beyond playing games: nephrologist vs machine in pediatric dialysis prescribing
topic Editorial Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6132900/
https://www.ncbi.nlm.nih.gov/pubmed/30003314
http://dx.doi.org/10.1007/s00467-018-4021-4
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