Using and understanding cross-validation strategies. Perspectives on Saeb et al

Area of Science:

• Machine Learning
• Clinical Applications
• Data Science

Background:

• Cross-validation is crucial for evaluating machine learning models in clinical settings.
• Approximating the real-world use-case during model evaluation presents significant challenges.
• Peer review highlights the need for nuanced approaches to cross-validation in healthcare.

Purpose of the Study:

• To critically examine the complexities and nuances of cross-validation techniques.
• To discuss the suitability and interpretation of various cross-validation strategies in clinical machine learning.
• To synthesize perspectives from reviewers and authors on best practices.

Main Methods:

• A three-part review format.
• Analysis of peer-reviewed feedback on a specific clinical machine learning paper.
• Discussion of author and reviewer viewpoints on cross-validation.

Main Results:

• Cross-validation in clinical machine learning is not a one-size-fits-all approach.
• The interpretation of cross-validation results must consider the specific clinical use-case.
• Different cross-validation strategies have varying degrees of suitability and potential biases.

Conclusions:

• Careful selection and interpretation of cross-validation methods are essential for reliable clinical machine learning models.
• Future work should focus on developing and validating cross-validation strategies tailored to specific clinical contexts.
• Bridging the gap between model performance and real-world clinical utility requires rigorous evaluation.