Decision Support for Rheumatoid Arthritis Using Bayesian Networks Diagnosis Management and Person

Decision-Support for Rheumatoid Arthritis Using Bayesian Networks: Diagnosis, Management, and Person

Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022.


Bayesian networks (BNs) have been widely proposed for medical decision support, perhaps because they can be built from knowledge and data. In my Ph.D., we examined how BNs can be used for decision-support challenges of chronic diseases, and we focused on Rheumatoid Arthritis (RA), as a case study. Three stages of this decision-support included diagnosis, self-management, and personalised care, with progressively less available data. For diagnosis, various criteria are proposed by clinicians for early diagnosis of RA, but these criteria are deterministic and cannot deal with diagnostic uncertainty. We built a BN model for diagnosing RA using an available dataset and experts’ knowledge. We obtained promising results (AUROC=0.84), and we compared them with those of an alternative BN model entirely learned from data (AUROC=0.71). We argued that a clinically meaningful structure of a BN model allows us to explain clinical scenarios in a way that cannot be done with the model learned entirely from data. For self-management, we intended to estimate disease activity remotely and frequently (e.g., weekly), instead of the current clinical practice of disease activity measurement once in 3 to 6 months, when an urgent visit and medication review may not be needed. We built two dynamic BN (DBN) models using experts’ knowledge and a set of manipulated data to predict appointment scheduling and medication review. Both models indicated acceptable performance; AUROC of the first DBN was 0.69, and AUROC of the second DBN was 0.66. The third stage of decision-support focused on personalised care for living with RA since it can have a profound impact on quality of life (QoL). We used experts’ knowledge and literature to build a BN that predicts QoL and helps to personalise the recommendations for enhancing QoL. The obtained recommendations for a set of scenarios were comparable with those of the experts.

Presentation Video

Presentation Slides

About the Presenter

Ali Fahmi is a post-doctoral researcher in statistics and epidemiology at the University of Manchester, United Kingdom. He holds a Ph.D. in computer science from Queen Mary University of London, an MSc in management engineering from Istanbul Technical University, Turkey, and a BSc in industrial engineering from the University of Tabriz, Iran. His Ph.D. research focused on creating decision support systems with causal Bayesian network models for diagnosis, self-management, and personalised care. Currently, he is doing research in the framework of the BRIT2 project, aiming to develop and evaluate a knowledge-support for prescribing antibiotics for common infections in primary care. This project also evaluates the indirect effect of the Covid19 pandemic on prescribing antibiotics for common infections. His main research interests are decision support systems and their applications in medicine and healthcare. His main extracurricular activity is designing carpet patterns and weaving carpets.

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