The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort

Abstract Purpose The availability of population datasets and machine learning techniques heralded a new era of sophisticated prediction models involving a large number of routinely collected variables. However, severe class imbalance in clinical datasets is a major challenge. The aim of this study i...

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Bibliografski detalji
Glavni autori:	Janny Xue Chen Ke, Arunachalam DhakshinaMurthy, Ronald B. George, Paula Branco
Format:	Artigo
Jezik:	Inglês
Izdano:	Springer 2024-11-01
Serija:	Discover Artificial Intelligence
Teme:	Machine learning Class imbalance Resampling Risk prediction Predictive modeling Anesthesiology
Online pristup:	https://doi.org/10.1007/s44163-024-00199-0
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The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort

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