Calibration: the Achilles heel of predictive analytics

Background The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Main text Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and poten...

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Vydané v:	BMC medicine Ročník 17; číslo 1; s. 230 - 7
Hlavní autori:	Van Calster, Ben, McLernon, David J., van Smeden, Maarten, Wynants, Laure, Steyerberg, Ewout W.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 16.12.2019 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Adult Aged Algorithms Analysis Artificial intelligence Biomedicine Calibration Calibration - standards Data mining Decision analysis Decision making Embryos Estimates Health care policy Health counseling Heterogeneity Humans In vitro fertilization Learning algorithms Machine learning Machine Learning - standards Male Medicine Medicine & Public Health Middle Aged Model performance Opinion Overfitting Patients Prediction models Predictive analytics Predictive Value of Tests Regression analysis Risk prediction models Shared decision making Heterogeneity Risk prediction models Model performance Calibration Predictive analytics Overfitting
ISSN:	1741-7015, 1741-7015
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Shrnutí:	Background The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Main text Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice. Conclusion Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1741-7015 1741-7015
DOI:	10.1186/s12916-019-1466-7