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Multiple chronic conditions (MCC) are one of the biggest challenges of moderntimes. The evolution of MCC follows a complex stochastic process that isinfluenced by a variety of risk factors, ranging from pre-existing conditionsto modifiable lifestyle behavioral factors (e.g. diet, exercise habits, tobaccouse, alcohol use, etc.) to non-modifiable socio-demographic factors (e.g., age,gender, education, marital status, etc.). People with MCC are at an increasedrisk of new chronic conditions and mortality. This paper proposes a modelpredictive control functional continuous time Bayesian network, an onlinerecursive method to examine the impact of various lifestyle behavioral changeson the emergence trajectories of MCC and generate strategies to minimize therisk of progression of chronic conditions in individual patients. The proposedmethod is validated based on the Cameron county Hispanic cohort (CCHC) dataset,which has a total of 385 patients. The dataset examines the emergence of 5chronic conditions (diabetes, obesity, cognitive impairment, hyperlipidemia,and hypertension) based on four modifiable risk factors representing lifestylebehaviors (diet, exercise habits, tobacco use, alcohol use) and fournon-modifiable risk factors, including socio-demographic information (age,gender, education, marital status). The proposed method is tested underdifferent scenarios (e.g., age group, the prior existence of MCC),demonstrating the effective intervention strategies for improving the lifestylebehavioral risk factors to offset MCC evolution.

A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions