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Clinical Time Series Prediction with a Hierarchical Dynamical System

  • Zitao Liu
  • Milos Hauskrecht
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)

Abstract

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.

Keywords

Time Series Gaussian Processes Linear Dynamical System 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zitao Liu
    • 1
  • Milos Hauskrecht
    • 1
  1. 1.Department of Computer ScienceUniversity of PittsburghPittsburghUSA

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