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The emergence and progression of multiple chronic conditions (MCC) over timeoften form a dynamic network that depends on patient's modifiable risk factorsand their interaction with non-modifiable risk factors and existing conditions.Continuous time Bayesian networks (CTBNs) are effective methods for modelingthe complex network of MCC relationships over time. However, CTBNs are not ableto effectively formulate the dynamic impact of patient's modifiable riskfactors on the emergence and progression of MCC. Considering a functional CTBN(FCTBN) to represent the underlying structure of the MCC relationships withrespect to individuals' risk factors and existing conditions, we propose anonlinear state-space model based on Extended Kalman filter (EKF) to capturethe dynamics of the patients' modifiable risk factors and existing conditionson the MCC evolution over time. We also develop a tensor control chart todynamically monitor the effect of changes in the modifiable risk factors ofindividual patients on the risk of new chronic conditions emergence. Wevalidate the proposed approach based on a combination of simulation and realdata from a dataset of 385 patients from Cameron County Hispanic Cohort (CCHC)over multiple years. The dataset examines the emergence of 5 chronic conditions(Diabetes, Obesity, Cognitive Impairment, Hyperlipidemia, and Hypertension)based on 4 modifiable risk factors representing lifestyle behaviors (Diet,Exercise, Smoking Habit, and Drinking Habit) and 3 non-modifiable risk factors,including demographic information (Age, Gender, Education). The resultsdemonstrate the effectiveness of the proposed methodology for dynamicprediction and monitoring of the risk of MCC emergence in individual patients.

Nonlinear State Space Modeling and Control of the Impact of Patients' Modifiable Lifestyle Behaviors on the Emergence of Multiple Chronic Conditions