Preliminary Study for a Bayesian Network Prognostic Model for Crohn's Disease

Crohn's disease is one type of inflammatory bowel disease whose incidence is currently increasing, and may affect any part of both the small and large intestine, possibly irritating deeper layers of the organs. Being a chronic disease, neither treatment nor surgery actually heals the patients. Thus, focus has been given to identifying good prognostic models based on clinical factors since they are more easily included in daily practice. The aim of this work is to provide an initial study on the adequacy of a Bayesian network model to enhance the prognosis prediction for patients with Crohn's disease. Multicentric study data of patients with surgery or immuno suppression in the six month after diagnosis was used to derive a Bayesian network, focusing on the prognosis and the analysis of factors interaction, including clinical features, disease course, treatment, follow-up plan, and adverse events. Two models were evaluated (naïve Bayes and Tree-Augmented Naïve Bayes) and also compared with logistic regression, using cross-validation and ROC curve analysis. Preliminary results showed competitive accuracy (above 75%) and discriminative power (above 70%). The generated models presented interesting insights on factor interaction and predictive ability for the prognosis, supporting their use in future clinical decision support systems.