A Functional Model for Structure Learning and Parameter Estimation in Continuous Time Bayesian Network: An Application in Identifying Patterns of Multiple Chronic Conditions
This work addresses disease modeling and prediction for patients with multiple chronic conditions, offering incremental improvements in structure learning and parameter estimation for continuous time Bayesian networks.
The authors tackled the problem of modeling complex relationships among multiple chronic conditions over time by proposing a continuous time Bayesian network with Poisson regression for conditional dependencies and an adaptive regularization method for structure learning. Their approach provided a sparse, intuitive representation and was evaluated on electronic health records, showing competitive performance for both short-term and long-term predictions compared to existing methods.
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.