Clinical Prediction Models

By H. Lavretsky, M. Sajatovic, Charles F. Reynolds III. 2013

600 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/27e59b5ed844fbc2eb0d4968a84455f55ee54096

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

1. Ewout W. Steyerberg
2. Karel G. M. Moons
3. Peter C. Austin

In Psychiatry and Mental health

1. Ewout W. Steyerberg
2. Peter C. Austin

Citations in the corpus

• Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers (2013, methodology): “An intercept and slope are then estimated, which are related to calibration-in-the-large and the calibration slope [8].”
• A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “The extracted items included general study characteristics, applied algorithms and their characteristics, data-driven variable selection, and model performance (Table A.1, Appendix B) [1,2,13,23 e 25].”
• Risk prediction models: II. External validation, model updating, and impact assessment (2012, methodology): “Methods for prediction model updating Several methods for updating prediction models have been proposed and evaluated (table 1).(3) 11 12 Most often, differences are seen in the outcome or event frequency between the development and new validation sample.”
• Intermediate and advanced topics in multilevel logistic regression analysis (2017, background): “, mortality) and one subject who did not experience the event of interest, the c-statistic is the proportion of such pairs in which the subject who experienced the event had a higher predicted probability of experiencing the event than did the subject who did not experience the event [38].”
• External validation of a Cox prognostic model: principles and methods (2013, background): “For logistic regression models, for instance, there are several well-recognised approaches [4-9].”
• Developing prediction models for clinical use using logistic regression: an overview. (2019, methodology): “Inherent model development begins at the point of data collection, as a well-performing risk prediction model requires some number of strong predictors being present (2,15).”
• Calibration: the Achilles heel of predictive analytics (2019, methodology): “The concepts explained in this section are illustrated in Additional file 1, with the validation of the Risk of Ovarian Malignancy Algorithm (ROMA) for the diagnosis of ovarian malignancy in women with an ovarian tumor selected for surgical removal [28]; further details can be found elsewhere [1, 4,”
• Does data splitting improve prediction? (2013, background): “Steyerberg (2009), Schumacher et al. (2007) and Molinaro et al. (2005) all advise against data splitting although the latter two references investigate cases where the number of variables is large relative to the number of observations.”
• Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. (2020, methodology): “30,31 To quantify the calibration, we computed calibration slopes and intercepts independently for each hourly prediction.”