Characterizing the Risk of Atrial Fibrillation in Cardiac Patients with Exceptional Electrocardiogram Phenotypes

We provide a transparent method to characterize Atrial Fibrillation (AF) caused by cardiac surgery, using Electrocardiogram (ECG) phenotypes. Current practice in the hospital is reactive rather than preventive and is based on a third party's proprietary alarms on vitals. Assistance with detection and prediction methods often lacks sufficient insights into their decisions toward the users, i.e., the hospital workers. This aspect is necessary to gain the trust of medical workers and patients in the decisions that are made. Our objective of transparently identifying risk factors for AF helps experts increase their understanding of the problem, and assists in decision-making about administering preventive medication to risk groups. With the deployment of the Exceptional Model Mining (EMM) framework on AF-related ECG phenotypes, we introduce a transparent and actionable method that assists the hospital in preventive treatment. We find several subgroups with EMM that align with known risk factors in the existing literature, confirming the ability of our method to identify risk groups of AF successfully. In addition, new hypotheses on found characteristics and combinations thereof have originated from the deployment. The hospital is advised to administer preventive medications to patients who match the descriptions of the risk groups found and perform follow-up clinical studies to validate the found hypotheses.