A Drift-Aware Adaptive Machine Learning Framework for Reliable Clinical Prediction in Dynamic Healthcare Environments
The rise of Machine Learning techniques in healthcare has greatly enhanced the prediction and decision-making tasks. But a key problem is the drift in the statistical characteristics of health care data, causing deterioration of the predictive models. Current solutions either rely on drift detection or retraining strat...