In this work we develop and test a novel hierarchical framework for modeling and learning multivariate clinical time series data. Our framework combines two modeling approaches: Linear Dynamical Systems (LDS) and Gaussian Processes (GP), and is capable to model and work with time series of varied length and with irregularly sampled observations. We test our framework on the problem of learning clinical time series data from the complete blood count panel, and show that our framework outperforms alternative time series models in terms of its predictive accuracy. © 2013 Springer-Verlag.
CITATION STYLE
Liu, Z., & Hauskrecht, M. (2013). Clinical time series prediction with a hierarchical dynamical system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7885 LNAI, pp. 227–237). Springer Verlag. https://doi.org/10.1007/978-3-642-38326-7_34
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