The myth of generalisability in clinical research and machine learning in health care

An emphasis on overly broad notions of generalisability as it pertains to applications of machine learning in health care can overlook situations in which machine learning might provide clinical utility. We believe that this narrow focus on generalisability should be replaced with wider consideratio...

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Autores principales: Futoma, Joseph, Simons, Morgan, Panch, Trishan, Doshi-Velez, Finale, Celi, Leo Anthony
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier Ltd. 2020
Materias:
Viewpoint
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7444947/
https://www.ncbi.nlm.nih.gov/pubmed/32864600
http://dx.doi.org/10.1016/S2589-7500(20)30186-2
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author Futoma, Joseph
Simons, Morgan
Panch, Trishan
Doshi-Velez, Finale
Celi, Leo Anthony
author_facet Futoma, Joseph
Simons, Morgan
Panch, Trishan
Doshi-Velez, Finale
Celi, Leo Anthony
author_sort Futoma, Joseph
collection PubMed
description An emphasis on overly broad notions of generalisability as it pertains to applications of machine learning in health care can overlook situations in which machine learning might provide clinical utility. We believe that this narrow focus on generalisability should be replaced with wider considerations for the ultimate goal of building machine learning systems that are useful at the bedside.
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spelling pubmed-74449472020-08-26 The myth of generalisability in clinical research and machine learning in health care Futoma, Joseph Simons, Morgan Panch, Trishan Doshi-Velez, Finale Celi, Leo Anthony Lancet Digit Health Viewpoint An emphasis on overly broad notions of generalisability as it pertains to applications of machine learning in health care can overlook situations in which machine learning might provide clinical utility. We believe that this narrow focus on generalisability should be replaced with wider considerations for the ultimate goal of building machine learning systems that are useful at the bedside. The Author(s). Published by Elsevier Ltd. 2020-09 2020-08-24 /pmc/articles/PMC7444947/ /pubmed/32864600 http://dx.doi.org/10.1016/S2589-7500(20)30186-2 Text en © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Viewpoint
Futoma, Joseph
Simons, Morgan
Panch, Trishan
Doshi-Velez, Finale
Celi, Leo Anthony
The myth of generalisability in clinical research and machine learning in health care
title The myth of generalisability in clinical research and machine learning in health care
title_full The myth of generalisability in clinical research and machine learning in health care
title_fullStr The myth of generalisability in clinical research and machine learning in health care
title_full_unstemmed The myth of generalisability in clinical research and machine learning in health care
title_short The myth of generalisability in clinical research and machine learning in health care
title_sort myth of generalisability in clinical research and machine learning in health care
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7444947/
https://www.ncbi.nlm.nih.gov/pubmed/32864600
http://dx.doi.org/10.1016/S2589-7500(20)30186-2
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