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 dynamic 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.