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

TL;DR

This paper introduces a continuous time Bayesian network model with adaptive regularization for analyzing disease progression in patients with multiple chronic conditions, enabling better prediction and understanding of disease trajectories.

Contribution

It presents a novel continuous time Bayesian network with Poisson regression dependencies and an adaptive regularization method for efficient structure learning.

Findings

Outperforms existing methods in predicting disease trajectories.

Provides a sparse, interpretable model of complex disease relationships.

Capable of analyzing multi-year disease progression patterns.

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.