Sleep Apnea Forecasting with Dynamic Network Models

Reference: Dagum, P. & Galper, A. Sleep Apnea Forecasting with Dynamic Network Models. Washington, D.C, 1993.

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.

Notes: Updated May 1993.

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