A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions

A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions

Bayesian networks are powerful statistical models to study the probabilistic relationships among sets of random variables with significant applications in disease modeling and prediction. Here, we propose a continuous time Bayesian network with conditional dependencies represented as regularized Poi...

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Bibliographic Details
Main Authors: Syed Hasib Akhter Faruqui, Adel Alaeddini, Jing Wang, Carlos A. Jaramillo, Mary Jo Pugh
Format: article
Language:EN
Published: IEEE 2021
Subjects:
Continuous time Bayesian network
Poisson regression
adaptive group lasso
Gaussian mixture model
multiple chronic conditions
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Online Access:https://doaj.org/article/e31ca8fda9884386b5a99d3ceef1fa70
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https://doaj.org/article/e31ca8fda9884386b5a99d3ceef1fa70

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