Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control

Background: Machine learning (ML) methods often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices in clinical settings, where data drift may cause unexpected performance that jeopardizes patient safety.
Method: We propose a ML-enabled Statistical Process Control (SPC) framework for out-of-distribution (OOD) detection and drift monitoring. SPC is advantageous as it visually and statistically highlights deviations from the expected distribution. To demonstrate the utility of the proposed framework for monitoring data drift …

clinical control cs.ai cs.lg data detection devices distribution drift eess.iv framework machine machine learning monitoring patient performance process safety spc statistical training