Estimating Real World Performance of a Predictive Model: A Case-Study in Predicting End-of-Life

Abstract

Objective: The main criteria for choosing how models are built is the subsequent effect on future (estimated) model performance. In this work, we evaluate the effects of experimental design choices on both estimated and actual model performance. Materials and Methods: Four years of hospital admissions are used to develop a 1 year end-of-life prediction model. Two common methods to select appropriate prediction timepoints (backwards-from-outcome and forwards-from-admission) are introduced and combined with two ways of separating cohorts for training and testing (internal and temporal). Two models are trained in identical conditions, and their performances are compared. Finally, operating thresholds are selected in each test set and applied in a final, ‘real-world’ cohort consisting of one year of admissions. Results: Backwards-from-outcome cohort selection discards 75% of candidate admissions (n=23,579), whereas forwards-from-admission selection includes many more (n=92,148). Both selection methods produce similar global performances when applied to an internal test set. However, when applied to the temporally defined ‘real-world’ set, forwards-from-admission yields higher areas under the ROC and precision recall curves (88.3 and 56.5% vs. 83.2 and 41.6%). Discussion: A backwards-from-outcome experiment effectively transforms the training data such that it no longer resembles real-world data. This results in optimistic estimates of test set performance, especially at high precision. In contrast, a forwards-from-admission experiment with a temporally separated test set consistently and conservatively estimates real-world performance. Conclusion: Experimental design choices impose bias upon selected cohorts. A forwards-from-admission experiment, validated temporally, can conservatively estimate real-world performance.

Publication
medRxiv
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Vincent Major
PhD Candidate

Vincent Major is a PhD Candidate studying Medical Informatics at NYU Langone Health. His research focuses on development, validation, and deployment of machine and deep learning models into clinical practice.

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