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Comparison of deep learning and conventional methods for disease onset prediction
Deep learning methods promise enhanced prediction performance by extracting complex patterns from clinical data, but face challenges like data sparsity and high dimensionality.
Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?
Objective: Provide guidance on sample size considerations for developing predictive models by empirically establishing the adequate sample size, which balances the competing objectives of improving model performance and reducing model complexity as well as computational requirements.
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but adoption in clinical practice is still limited.
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