Beyond performance metrics: modeling outcomes and cost for clinical machine learning

Abstract Advances in medical machine learning are expected to help personalize care, improve outcomes, and reduce wasteful spending. In quantifying potential benefits, it is important to account for constraints arising from clinical workflows. Practice variation is known to influence the accuracy an...

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Autores principales:	James A. Diao, Leia Wedlund, Joseph Kvedar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/400b609441b14c7abb99aa419f20a394
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spelling	oai:doaj.org-article:400b609441b14c7abb99aa419f20a3942021-12-02T19:06:43ZBeyond performance metrics: modeling outcomes and cost for clinical machine learning10.1038/s41746-021-00495-42398-6352https://doaj.org/article/400b609441b14c7abb99aa419f20a3942021-08-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00495-4https://doaj.org/toc/2398-6352Abstract Advances in medical machine learning are expected to help personalize care, improve outcomes, and reduce wasteful spending. In quantifying potential benefits, it is important to account for constraints arising from clinical workflows. Practice variation is known to influence the accuracy and generalizability of predictive models, but its effects on cost-effectiveness and utilization are less well-described. A simulation-based approach by Mišić and colleagues goes beyond simple performance metrics to evaluate how process variables may influence the impact and financial feasibility of clinical prediction algorithms.James A. DiaoLeia WedlundJoseph KvedarNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 James A. Diao Leia Wedlund Joseph Kvedar Beyond performance metrics: modeling outcomes and cost for clinical machine learning
description	Abstract Advances in medical machine learning are expected to help personalize care, improve outcomes, and reduce wasteful spending. In quantifying potential benefits, it is important to account for constraints arising from clinical workflows. Practice variation is known to influence the accuracy and generalizability of predictive models, but its effects on cost-effectiveness and utilization are less well-described. A simulation-based approach by Mišić and colleagues goes beyond simple performance metrics to evaluate how process variables may influence the impact and financial feasibility of clinical prediction algorithms.
format	article
author	James A. Diao Leia Wedlund Joseph Kvedar
author_facet	James A. Diao Leia Wedlund Joseph Kvedar
author_sort	James A. Diao
title	Beyond performance metrics: modeling outcomes and cost for clinical machine learning
title_short	Beyond performance metrics: modeling outcomes and cost for clinical machine learning
title_full	Beyond performance metrics: modeling outcomes and cost for clinical machine learning
title_fullStr	Beyond performance metrics: modeling outcomes and cost for clinical machine learning
title_full_unstemmed	Beyond performance metrics: modeling outcomes and cost for clinical machine learning
title_sort	beyond performance metrics: modeling outcomes and cost for clinical machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/400b609441b14c7abb99aa419f20a394
work_keys_str_mv	AT jamesadiao beyondperformancemetricsmodelingoutcomesandcostforclinicalmachinelearning AT leiawedlund beyondperformancemetricsmodelingoutcomesandcostforclinicalmachinelearning AT josephkvedar beyondperformancemetricsmodelingoutcomesandcostforclinicalmachinelearning
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Beyond performance metrics: modeling outcomes and cost for clinical machine learning

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