Evaluation of clinical prediction models (part 1): from development to external validation
Gary S. Collins, Paula Dhiman, Jie Ma, Michael Maia Schlüssel, Lucinda Archer, Ben Van Calster, Frank E. Harrell, Glen P. Martin, Karel G. M. Moons, Maarten van Smeden, Matthew Sperrin, Garrett S. Bullock, Richard D Riley
Abstract
Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance.
Topics & Concepts
Computer sciencePredictive modellingExternal validityData miningMachine learningStatisticsMathematicsMachine Learning in HealthcareSepsis Diagnosis and TreatmentMeta-analysis and systematic reviews