Making machine learning matter to clinicians: model actionability in medical decision-making.

Autor: Ehrmann DE; Department of Critical Care Medicine and Labatt Family Heart Centre, The Hospital for Sick Children, Toronto, ON, Canada. Dehrmann@umich.edu.; Congenital Heart Center at Mott Children's Hospital and the University of Michigan Medical School, Ann Arbor, MI, USA. Dehrmann@umich.edu., Joshi S; Center for Research on Computation on Society, Harvard University, Cambridge, MA, USA., Goodfellow SD; Department of Critical Care Medicine and Labatt Family Heart Centre, The Hospital for Sick Children, Toronto, ON, Canada.; Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada., Mazwi ML; Department of Critical Care Medicine and Labatt Family Heart Centre, The Hospital for Sick Children, Toronto, ON, Canada.; Department of Paediatrics, University of Toronto, Toronto, ON, Canada., Eytan D; Department of Critical Care Medicine and Labatt Family Heart Centre, The Hospital for Sick Children, Toronto, ON, Canada.; Department of Medicine, Technion, Haifa, Israel.
Jazyk: angličtina
Zdroj: NPJ digital medicine [NPJ Digit Med] 2023 Jan 24; Vol. 6 (1), pp. 7. Date of Electronic Publication: 2023 Jan 24.
DOI: 10.1038/s41746-023-00753-7
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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