Clinical Prediction Models

By Ewout W. Steyerberg. Statistics for Biology and Health, 2008

2,501 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/977fe372357b9b5cb664992e54e7613a3ec42ed6

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Citations in the corpus

Language Models are Few-shot Learners for Prognostic Prediction (2023, background): “In order to facilitate medical decision-making and health outcomes, clinical prediction models (Steyerberg, 2008; Smeden et al., 2021) Figure 1: Pilot study .”
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “Third,we use theAreaUnder the receiver operator characteristic Curve (AUC) to measure the discriminatory power of our models, as suggested by recent research [58], [74], [75], [76], [78], [79].”
Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes (2018, methodology): “ln ( pi 1−pi ) = α + S(?̂?1X1i + ?̂?2X2i + ?̂?3X3i + ⋯ ) (1)”
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, methodology): “For example, logistic model revision regresses the outcome against the prediction from the original model and other shift-prone variables 83 .”
A Commentary on Statistical Assessment of Violence Recidivism Risk (2015, methodology): “e subject of extensive statistical research, yielding numerous direct methods unrecognized by and superseding these authors’ condence interval approach (Harrell et al. 1996; Harrell 2001; Pepe 2004; Steyerberg 2009a; Zhou et al. 2011). 8 If focus on variability of the R^ G and their linkage to indiv”
A tutorial on calibration measurements and calibration models for clinical prediction models (2020, background): “In contrast to discrimination, which refers to the ability of a model to rank patients according to risk, calibration refers to the agreement between the estimated and the “true” risk of an outcome.(3) A well-calibrated model is one that minimizes residuals, which is equivalent to saying that the mo”
Determinants of COVID-19 vaccine acceptance in the US (2020, methodology): “We used bootstrap resampling (1000 samples) for internal validation and to obtain an area under the curve value accounting for model optimism.(8,9) Data were analyzed using Stata version 16 (StataCorp, College Station, Texas).”
The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “2 Threshold-Independent Performance Measures First, we use the Area Under the receiver operator characteristic Curve (AUC) to measure the discriminatory power of our models, as suggested by recent research [26, 49, 58, 81, 130, 131].”