Forecasting sleep apnea with dynamic network models

  • Authors:

  • Paul Dagum;Adam Galper

  • Affiliations:

  • Medical Informatics, Stanford University School of Medicine and Rockwell Palo Alto Laboratory, Palo Alto, California;Medical Informatics, Stanford University School of Medicine, Stanford, California

  • Venue:
  • UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
  • Year:
  • 1993

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Abstract

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.