Empirical investigation of multi-source cross-validation in clinical machine learning

arXiv:2403.15012v1 Announce Type: new
Abstract: Traditionally, machine learning-based clinical prediction models have been trained and evaluated on patient data from a single source, such as a hospital. Cross-validation methods can be used to estimate the accuracy of such models on new patients originating from the same source, by repeated random splitting of the data. However, such estimates tend to be highly overoptimistic when compared to accuracy obtained from deploying models to sources not represented in the dataset, such as a …

abstract accuracy arxiv clinical cs.lg data hospital investigation machine machine learning patient patients prediction prediction models random stat.ml type validation