Diagnostic and prognostic prediction models

By J. Hendriksen, G. Geersing, Karel G. M. Moons, J. Groot. 2013

217 citations recorded by the source corpus.

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AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020, background): “As the characteristics of the population evolve, the adjustment and updating of risk scores are needed, which are time-consuming and inflexible [49].”
Bias in artificial intelligence algorithms and recommendations for mitigation (2023, methodology): “Prediction algorithms and models use various types of data such as patient-specific demographics and disease characteristics to estimate the probability of having (diagnostic prediction) or developing (prognostic prediction) a particular disease or specific outcome [1].”
Developing prediction models for clinical use using logistic regression: an overview. (2019, methodology): “However, if a particular variable is frequently missing one must consider that it may also frequently be unobtainable in the general population for which the model is intended and thus may not be an ideal predictor to include in the model (3,10).”