Modeling Clinical Time Series Using Gaussian Process Sequences

Abstract

Development of accurate models of complex clinical time series data is critical for understanding the disease, its dynamics, and subsequently patient management and clinical decision making. Clinical time series differ from other time series applications mainly in that observations are often missing and made at irregular time intervals. In this work, we propose and test a new probabilistic approach for modeling clinical time series data that is optimized to handle irregularly sampled observations. Our model is defined by a sequence of Gaussian processes (GPs), each restricted to a window of a finite size, where dependencies among two consecutive Gaussian processes are represented using a linear dynamical system. We develop algorithms supporting both model learning and inference. Experiments on real-world clinical time series data show that our model is better for modeling clinical time series and that it outperforms or is close to alternative time series prediction models.

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