A Functional Model for Structure Learning and Parameter Estimation in
Continuous Time Bayesian Network: An Application in Identifying Patterns of
Multiple Chronic Conditions
- URL: http://arxiv.org/abs/2007.15847v2
- Date: Wed, 14 Jul 2021 21:03:44 GMT
- Title: A Functional Model for Structure Learning and Parameter Estimation in
Continuous Time Bayesian Network: An Application in Identifying Patterns of
Multiple Chronic Conditions
- Authors: Syed Hasib Akhter Faruqui, Adel Alaeddini, Jing Wang, and Carlos A.
Jaramillo
- Abstract summary: We propose a continuous time Bayesian network with conditional dependencies, represented as Poisson regression.
We use a dataset of patients with multiple chronic conditions extracted from electronic health records of the Department of Veterans Affairs.
The proposed approach provides a sparse intuitive representation of the complex functional relationships between multiple chronic conditions.
- Score: 2.440763941001707
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Bayesian networks are powerful statistical models to study the probabilistic
relationships among set random variables with major applications in disease
modeling and prediction. Here, we propose a continuous time Bayesian network
with conditional dependencies, represented as Poisson regression, to model the
impact of exogenous variables on the conditional dependencies of the network.
We also propose an adaptive regularization method with an intuitive early
stopping feature based on density based clustering for efficient learning of
the structure and parameters of the proposed network. Using a dataset of
patients with multiple chronic conditions extracted from electronic health
records of the Department of Veterans Affairs we compare the performance of the
proposed approach with some of the existing methods in the literature for both
short-term (one-year ahead) and long-term (multi-year ahead) predictions. The
proposed approach provides a sparse intuitive representation of the complex
functional relationships between multiple chronic conditions. It also provides
the capability of analyzing multiple disease trajectories over time given any
combination of prior conditions.
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