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Bayesian networks are powerful statistical models to study the probabilisticrelationships among set random variables with major applications in diseasemodeling and prediction. Here, we propose a continuous time Bayesian networkwith conditional dependencies, represented as Poisson regression, to model theimpact of exogenous variables on the conditional dependencies of the network.We also propose an adaptive regularization method with an intuitive earlystopping feature based on density based clustering for efficient learning ofthe structure and parameters of the proposed network. Using a dataset ofpatients with multiple chronic conditions extracted from electronic healthrecords of the Department of Veterans Affairs we compare the performance of theproposed approach with some of the existing methods in the literature for bothshort-term (one-year ahead) and long-term (multi-year ahead) predictions. Theproposed approach provides a sparse intuitive representation of the complexfunctional relationships between multiple chronic conditions. It also providesthe capability of analyzing multiple disease trajectories over time given anycombination of prior conditions.

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