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

Ioan Stanculescu, Christopher K.I. Williams, Yvonne Freer
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:755-764, 2014.

Abstract

In this paper we develop a Hierarchi- cal Switching Linear Dynamical System (HSLDS) for the detection of sepsis in neonates in an intensive care unit. The Fac- torial 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 ar- tifactual origin. In this paper we demonstrate that by adding a higher-level discrete variable with semantics sepsis/non-sepsis we can de- tect 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 differ- ent from the auto-regressive HMM of Stan- culescu et al. (2013), despite the fact that their model is given “ground truth” annota- tions of the physiological factors, while our HSLDS must infer them from the raw vital signs data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-stanculescu14a, title = {A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring}, author = {Stanculescu, Ioan and Williams, Christopher K.I. and Freer, Yvonne}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {755--764}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/stanculescu14a/stanculescu14a.pdf}, url = {https://proceedings.mlr.press/r12/stanculescu14a.html}, abstract = {In this paper we develop a Hierarchi- cal Switching Linear Dynamical System (HSLDS) for the detection of sepsis in neonates in an intensive care unit. The Fac- torial 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 ar- tifactual origin. In this paper we demonstrate that by adding a higher-level discrete variable with semantics sepsis/non-sepsis we can de- tect 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 differ- ent from the auto-regressive HMM of Stan- culescu et al. (2013), despite the fact that their model is given “ground truth” annota- tions of the physiological factors, while our HSLDS must infer them from the raw vital signs data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring %A Ioan Stanculescu %A Christopher K.I. Williams %A Yvonne Freer %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-stanculescu14a %I PMLR %P 755--764 %U https://proceedings.mlr.press/r12/stanculescu14a.html %V R12 %X In this paper we develop a Hierarchi- cal Switching Linear Dynamical System (HSLDS) for the detection of sepsis in neonates in an intensive care unit. The Fac- torial 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 ar- tifactual origin. In this paper we demonstrate that by adding a higher-level discrete variable with semantics sepsis/non-sepsis we can de- tect 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 differ- ent from the auto-regressive HMM of Stan- culescu et al. (2013), despite the fact that their model is given “ground truth” annota- tions of the physiological factors, while our HSLDS must infer them from the raw vital signs data. %Z Reissued by PMLR on 04 October 2026.
APA
Stanculescu, I., Williams, C.K. & Freer, Y.. (2014). A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:755-764 Available from https://proceedings.mlr.press/r12/stanculescu14a.html. Reissued by PMLR on 04 October 2026.

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