Learning Outcome-Discriminative Dynamics in Multivariate Physiological Cohort Time Series

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Learning Outcome-Discriminative Dynamics in Multivariate Physiological Cohort Time Series

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Title: Learning Outcome-Discriminative Dynamics in Multivariate Physiological Cohort Time Series
Author: Nemati, Shamim; Lehman, Li-wei H.; Adams, Ryan Prescott

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Citation: Nemati, Shamim, Li-wei H. Lehman, and Ryan P. Adams. 2013. Learning outcome-discriminative dynamics in multivariate physiological cohort time series. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 7104-7107. New York: Institute of Electrical and Electronics Engineers.
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Abstract: Model identification for physiological systems is complicated by changes between operating regimes and measurement artifacts. We present a solution to these problems by assuming that a cohort of physiological time series is generated by switching among a finite collection of physiologically-constrained dynamical models and artifactual segments. We model the resulting time series using the switching linear dynamical systems (SLDS) framework, and present a novel learning algorithm for the class of SLDS, with the objective of identifying time series dynamics that are predictive of physiological regimes or outcomes of interest. We present exploratory results based on a simulation study and a physiological classification example of decoding postural changes from heart rate and blood pressure. We demonstrate a significant improvement in classification over methods based on feature learning via expectation maximization. The proposed learning algorithm is general, and can be extended to other applications involving state-space formulations.
Published Version: doi:10.1109/EMBC.2013.6611195
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11326228
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