Change-point detection method for clinical decision support system rule monitoring

Publication date: Available online 3 July 2018
Source:Artificial Intelligence in Medicine
Author(s): Siqi Liu, Adam Wright, Milos Hauskrecht
A clinical decision support system (CDSS) helps clinicians to manage patients, but malfunctions of its components or other systems on which it depends may affect its intended functions. Monitoring the system and detecting changes in its behavior that may indicate the malfunction can help to avoid any potential costs associated with its improper operation. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts. We aim to screen and detect changes in real-time, that is whenever a new datum (rule firing count) arrives, we want to have a score indicating how likely there is a change in the system. We develop a new method based on Seasonal-Trend decomposition with locally weighted regression (Loess) and likelihood ratio statistics to detect the changes. Experiments on daily rule-firing-count data collected from a real CDSS and known change-points show that our method improves the detection performance when compared with existing change-point detection methods.



https://ift.tt/2KLJx24