Change-point detection for monitoring clinical decision support systems with a multi-process dynamic linear model

A clinical decision support system and its components may malfunction due to different reasons. The objective of this work is to develop computational methods that can help us to monitor the system and assure its proper operation by promptly detecting and analyzing changes in its behavior. We develop a new change-point detection method using the Multi-Process Dynamic Linear Model. The experiments on real and simulated data show that our method outperforms existing change-point detection methods, leading to higher accuracy and shorter delay in the detection.