  Gut microbial composition has been linked to multiple health outcomes. Yet,
temporal analysis of this composition had been limited to deterministic models.
In this paper, we introduce a probabilistic model for the dynamics of
intestinal microbiomes that takes into account interaction among bacteria as
well as external effects such as antibiotics. The model successfully deals with
pragmatic issues such as random measurement error and varying time intervals
between measurements through latent space modeling. We demonstrate utility of
the model by using latent state features to predict the clinical events of
intestinal domination and bacteremia, improving accuracy over existing methods.
We further leverage this framework to validate known links between antibiotics
and clinical outcomes, while discovering new ones.
