Sleep Apnea Forecasting with Dynamic Network Models

Citation: Paul Dagum and Adam Galper. (1993) Sleep Apnea Forecasting with Dynamic Network Models. In KSL-93-20, 1993.

Publication techreport ( Edit )
type	Technical Report
bibtype	techreport
Bibtex basics
author	Paul Dagum and Adam Galper
title	Sleep Apnea Forecasting with Dynamic Network Models
number	KSL-93-20
institution	Knowledge Systems, AI Laboratory
address	Washington, D.C
year	1993
Bibtex more
note	Updated May 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.

KSL Technical Report ID: KSL-93-20

Abstract	Dynamic network models (DNMs) are belief n … 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. found in traditional time-series analyses.
Address	Washington, D.C +
Author	Paul Dagum and Adam Galper +
Bibtype	techreport +
Has author	Paul Dagum and Adam Galper +
Has identifier	KSL-93-20 +
Has publishing details	1993 +
Has title	Sleep Apnea Forecasting with Dynamic Network Models +
Has where published	KSL-93-20 +
Has year	1993 +
Institution	Knowledge Systems, AI Laboratory +
Ksl tr id	KSL-93-20 +
Note	Updated May 1993.
Number	KSL-93-20 +
Process note	YES +
Title	Sleep Apnea Forecasting with Dynamic Network Models +
Year	1993 +