ArticleJMIR AI2023
Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial.
Article in JMIR AI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 3 of them syntheses that pooled it.
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Who cites it
78 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- Diagnostic utility of speech-based biomarkers in mild cognitive impairment: a systematic review and meta-analysis.Age and ageing · 2025Pooled it
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- Multiparametric PET/CT Tensor Radiomics for Stability-Aware Machine Learning in Lung Cancer Survival Prediction.Journal of imaging informatics in medicine · 2026Article
- Deep learning classification of nasal anatomical variants on computed tomography for endoscopic sinus surgery.Scientific reports · 2026Article
- Explainable Machine Learning for Predicting Dengue Recovery Duration: Insights from Multi-Center Clinical Data.Healthcare (Basel, Switzerland) · 2026Article
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- Automated Deep Learning Quantification of Avascular Area and Intravitreal Neovascularization in Retinal Flatmounts of Rodent Oxygen-Induced Retinopathy Models.Translational vision science & technology · 2026Article
- Data-driven detection of lucid episodes in advanced dementia: Application of latent class analysis to observational data.Journal of Alzheimer's disease : JAD · 2026Article
18 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
Cross-validation remains a popular means of developing and validating artificial intelligence for health care. Numerous subtypes of cross-validation exist. Although tutorials on this validation strategy have been published and some with applied examples, we present here a practical tutorial comparing multiple forms of cross-validation using a widely accessible, real-world electronic health care data set: Medical Information Mart for Intensive Care-III (MIMIC-III). This tutorial explored methods such as K-fold cross-validation and nested cross-validation, highlighting their advantages and disadvantages across 2 common predictive modeling use cases: classification (mortality) and regression (length of stay). We aimed to provide readers with reproducible notebooks and best practices for modeling with electronic health care data. We also described sets of useful recommendations as we demonstrated that nested cross-validation reduces optimistic bias but comes with additional computational challenges. This tutorial might improve the community's understanding of these important methods while catalyzing the modeling community to apply these guides directly in their work using the published code.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.