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