Forecasting Sleep Apnea with Dynamic Network Models
  Dynamic network models (DNMs) are belief networks for temporal reasoning. The
DNM methodology combines techniques from time series analysis and probabilistic
reasoning to provide (1) a knowledge representation that integrates
noncontemporaneous and contemporaneous dependencies and (2) methods for
iteratively refining these dependencies in response to the effects of exogenous
influences. We use belief-network inference algorithms to perform forecasting,
control, and discrete event simulation on DNMs. The belief network formulation
allows us to move beyond the traditional assumptions of linearity in the
relationships among time-dependent variables and of normality in their
probability distributions. We demonstrate the DNM methodology on an important
forecasting problem in medicine. We conclude with a discussion of how the
methodology addresses several limitations found in traditional time series
analyses.
