Towards better clinical prediction models: seven steps for development and an ABCD for validation.

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A tutorial on calibration measurements and calibration models for clinical prediction models (2020, background): “10 We provide here some simple and interpretable calibration measures and calibration models that can illustrate the concepts and have appeared in the recent biomedical predictive modeling literature so that clinical researchers and informaticians may familiarize themselves with this topic.”
Prediction models for cardiovascular disease risk in the general population: systematic review (2016, neutral): “We differentiated between articles reporting on the development 17-19 or external validation 19-21 of one or more prediction models (box 1).”
Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges (2016, background): “Recently, Steyerberg and Vergouwe 9 laid out seven steps for developing prediction models.”
Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, methodology): “Other suggested strategies to combat over ﬁ tting are dimensionality reduction 47,76 , which can be implicit (e.g., common in neural networks) 76 , and explicit penalization of complexity (e.g., regularization) 17,49,76 .”