Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions

Monitoring the performance of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very target that the algorithm aims to predict. Ignoring CMI by monitoring only the untreated patients--whose outcomes remain unaltered--can inflate false alarm rates, because the evolution of both the model and clinician-ML interactions can induce complex dependencies in the data that violate standard assumptions. A more sophisticated approach is to explicitly account for CMI by modeling treatment propensities, but its time-varying nature makes accurate estimation difficult. Given the many sources of complexity in the data, it is important to determine situations in which a simple procedure that ignores CMI provides valid inference. Here we describe the special case of monitoring model calibration, under either the assumption of conditional exchangeability or time-constant selection bias. We introduce a new score-based cumulative sum (CUSUM) chart for monitoring in a frequentist framework and review an alternative approach using Bayesian inference. Through simulations, we investigate the benefits of combining model updating with monitoring and study when over-trust in a prediction model does (or does not) delay detection. Finally, we simulate monitoring an ML-based postoperative nausea and vomiting risk calculator during the COVID-19 pandemic.