Abstract
A clinical decision support system (CDSS) and its components can malfunction due to various reasons. Monitoring the system and detecting its malfunctions can help one to avoid any potential mistakes and associated costs. 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. The detection should be performed online, that is whenever a new datum 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 and likelihood ratio statistics to detect the changes. Experiments on real and simulated data show that our method has a lower delay in detection compared with existing change-point detection methods.
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Acknowledgement
This research was supported by grants R01-LM011966 and R01-GM088224 from the NIH. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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Liu, S., Wright, A., Hauskrecht, M. (2017). Change-Point Detection Method for Clinical Decision Support System Rule Monitoring. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_14
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DOI: https://doi.org/10.1007/978-3-319-59758-4_14
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