Probabilistic Inference of Comorbidities from Symptoms in Patients with Atrial Fibrillation: An Ontology-Driven Hybrid Clinical Decision Support System

Atrial fibrillation
$(AF)$
is the most prevalent cardiac arrhythmia. While
$AF$
is a cardiological disease, its risk factors and mechanisms are often rooted in non-cardiological comorbidities, introducing complexity in the treatment of the heterogeneous patient population. This study presents the development of a clinical decision support system (CDSS), which aims to mitigate potential challenges of the cross-disciplinarity of
$AF$
A knowledge base is implemented to capture the hierarchical nature of relevant concepts.
$Nai\dot{v}e$
Bayes classifiers are used to predict the patient comorbidities related to
$AF$
mechanisms and risk factors based on provided symptoms. The resulting CDSS infers comorbidities with a top-k accuracy of 0.53, 0.80, and 0.88 for
$k=1, 3$
, and 5 respectively.