Making machine learning matter to clinicians: model actionability in medical decision-making.
Journal
NPJ digital medicine
ISSN: 2398-6352
Titre abrégé: NPJ Digit Med
Pays: England
ID NLM: 101731738
Informations de publication
Date de publication:
24 Jan 2023
24 Jan 2023
Historique:
received:
01
04
2022
accepted:
13
01
2023
entrez:
23
1
2023
pubmed:
24
1
2023
medline:
24
1
2023
Statut:
epublish
Résumé
Machine learning (ML) has the potential to transform patient care and outcomes. However, there are important differences between measuring the performance of ML models in silico and usefulness at the point of care. One lens to use to evaluate models during early development is actionability, which is currently undervalued. We propose a metric for actionability intended to be used before the evaluation of calibration and ultimately decision curve analysis and calculation of net benefit. Our metric should be viewed as part of an overarching effort to increase the number of pragmatic tools that identify a model's possible clinical impacts.
Identifiants
pubmed: 36690689
doi: 10.1038/s41746-023-00753-7
pii: 10.1038/s41746-023-00753-7
pmc: PMC9871014
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
7Informations de copyright
© 2023. The Author(s).
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