Dynamic Bayesian network models for self-management of chronic diseases: Rheumatoid arthritis case-study

Dynamic Bayesian Networks (DBNs) are temporal probabilistic graphical models with a set of random variables and dependencies between them. DBNs have a meaningful structure and can model the continuity of events in discrete time-slices. In this study, we aimed to show how to build DBN models for self-management of chronic diseases using multiple sources of evidence. Chronic diseases need a life-long treatment. People with chronic diseases are commonly provided fixed-interval clinic visits, but they can suffer from sudden increases of disease activity. We proposed an approach to build DBN models for self-management of chronic diseases in order to advise on treatment decisions. We used Rheumatoid Arthritis (RA) as a case-study, and employed rheumatology experts' knowledge, clinical data, clinical guidelines, and established literature to identify the variables, their states, dependencies between the variables, and parameters of the model. Due to the unavailability of the ideal data (i.e., large data with enough frequency), we adopted two approaches to make inferences for initial evaluation of the model: manipulation of the clinical data to increase their frequency and creating dummy patient scenarios. The initial evaluation indicated promising results for treatment decisions. The proposed approach used multiple sources of evidence to build DBN models for self-management of chronic diseases. The resulting DBN for RA case-study had a clinically meaningful structure, although it needed to be further evaluated and calibrated. Resulting DBN model has the potential to be used as a decision-support tool to help patients and clinicians better manage RA.