ArticleIEEE access : practical innovations, open solutions2021
Dynamic Functional Continuous Time Bayesian Networks for Prediction and Monitoring of the Impact of Patients' Modifiable Lifestyle Behaviors on the Emergence of Multiple Chronic Conditions.
Article in IEEE access : practical innovations, open solutions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 2 citations in OpenAlex.
- A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network.IEEE journal of biomedical and health informatics · 2025Article
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5 authors at 3 institutions in 1 country.
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Abstract
More than a quarter of all Americans are estimated to have multiple chronic conditions (MCC). It is known that shared modifiable lifestyle behaviors account for many common MCC. What is not precisely known is the dynamic effect of changes in lifestyle behaviors on the trajectories of MCC emergence. This paper proposes dynamic functional continuous time Bayesian networks to effectively formulate the dynamic effect of patients' modifiable lifestyle behaviors and their interaction with non-modifiable demographics and preexisting conditions on the emergence of MCC. The proposed method considers the parameters of the conditional dependencies of MCC as a nonlinear state-space model and develops an extended Kalman filter to capture the dynamics of the modifiable risk factors on the MCC evolution. It also develops a tensor-based control chart based on the integration of multilinear principal component analysis and multivariate exponentially weighted moving average chart to monitor the effect of changes in the modifiable risk factors on the risk of new MCC. We validate the proposed method based on a combination of simulation and a real dataset of 385 patients from the Cameron County Hispanic Cohort. The dataset examines the emergence of 5 chronic conditions (Diabetes, Obesity, Cognitive Impairment, Hyperlipidemia, Hypertension) based on 4 modifiable lifestyle behaviors representing (Diet, Exercise, Smoking Habits, Drinking Habits) and 3 non-modifiable demographic risk factors (Age, Gender, Education). For the simulated study, the proposed algorithm shows a run-length of 4 samples (4 months) to identify behavioral changes with significant impacts on the risk of new MCC. For the real data study, the proposed algorithm shows a run-length of one sample (one year) to identify behavioral changes with significant impacts on the risk of new MCC. The results demonstrate the sensitivity of the proposed methodology for dynamic prediction and monitoring of the risk of MCC emergence in individual patients.
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