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A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring

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Original languageEnglish
Title of host publicationProceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014)
Number of pages10
Publication statusPublished - 2014


In this paper we develop a Hierarchical Switching Linear Dynamical System (HSLDS) for the detection of sepsis in neonates in an intensive care unit. The Factorial Switching LDS (FSLDS) of Quinn et al. (2009) is able to describe the observed vital signs data in terms of a number of discrete factors, which have either physiological or artifactual origin. In this paper we demonstrate that by adding a higher-level discrete variable with semantics sepsis/non-sepsis we can detect changes in the physiological factors that signal the presence of sepsis. We demonstrate that the performance of our model for the detection of sepsis is not statistically different from the auto-regressive HMM of Stanculescu et al. (2013), despite the fact that their model is given "ground truth" annotations of the physiological factors, while our HSLDS must infer them from the raw vital signs data.

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