  We present a Discriminative Switching Linear Dynamical System (DSLDS) applied
to patient monitoring in Intensive Care Units (ICUs). Our approach is based on
identifying the state-of-health of a patient given their observed vital signs
using a discriminative classifier, and then inferring their underlying
physiological values conditioned on this status. The work builds on the
Factorial Switching Linear Dynamical System (FSLDS) (Quinn et al., 2009) which
has been previously used in a similar setting. The FSLDS is a generative model,
whereas the DSLDS is a discriminative model. We demonstrate on two real-world
datasets that the DSLDS is able to outperform the FSLDS in most cases of
interest, and that an $\alpha$-mixture of the two models achieves higher
performance than either of the two models separately.
