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.

Repeated Cross-Sectional (RCS) data measures a phenomenon by repeatedly sampling new cases from a population at successive measurement moments. It allows for analyzing societal trends without the need to follow individuals. To gain a deeper understanding of these trends, we propose EMM-RCS, an Exceptional Model Mining instance designed to find subgroups displaying exceptional trend behavior in RCS data. We build quality measures on the standard error, finding various types of exceptionalities within trends (exceptional flattening, slope, deviation from the norm). Additionally, EMM-RCS can handle practical RCS data problems, including uneven spacing of measurements over time, fluctuating sample sizes, and missing data.

Abstract Discrete Markov chains are frequently used to analyse transition behaviour in sequential data. Here, the transition probabilities can be estimated using varying order Markov chains, where order k specifies the length of the sequence history that is used to model these probabilities. Generally, such a model is fitted to the entire dataset, but in practice it is likely that some heterogeneity in the data exists and that some sequences would be better modelled with alternative parameter values, or with a Markov chain of a different order. We use the framework of Exceptional Model Mining (EMM) to discover these exceptionally behaving sequences. In particular, we propose an EMM model class that allows for discovering subgroups with transition behaviour of varying order. To that end, we propose three new quality measures based on information-theoretic scoring functions. Our findings from controlled experiments show that all three quality measures find exceptional transition behaviour of varying order and are reasonably sensitive. The quality measure based on Akaike’s Information Criterion is most robust for the number of observations. We furthermore add to existing work by seeking for subgroups of sequences, as opposite to subgroups of transitions. Since we use sequence-level descriptive attributes, we form subgroups of entire sequences, which is practically relevant in situations where you want to identify the originators of exceptional sequences, such as patients. We show this relevance by analysing sequences of blood glucose values of adult persons with diabetes type 2. In the experiments, we find subgroups of patients based on age and glycated haemoglobin (HbA1c), a measure known to correlate with average blood glucose values. Clinicians and domain experts confirmed the transition behaviour as estimated by the fitted Markov chain models.