## 2 Related Work  
The related work can be classified in the following four areas: (i) outcome-oriented predictive monitoring in process mining, (ii) interpretable (explainable) predictive models in process mining, (iii) association rule mining in process mining, and (iv) interpretable (or explainable) classification with association rule mining. Regarding outcome-oriented predictive monitoring, [PERSON] et al. [10] provide a general framework for outcome-based process monitoring and an insightful benchmark comparing the performance of different approaches from the perspective of sequence encoding, trace bucketing, and classification algorithms. Notable approaches in this field are the ones presented by [PERSON] et al. [11], which adopts trace clustering before predictive classification on running cases, and [PERSON] et al. [12], which adopts inter-case feature encoding to capture case inter action scenarios while predicting the remaining time of patient treatments. More recently, [PERSON] et al. [13] have suggested an approach based on deep learning, i.e., bidirectional LSTM with attention mechanism, that outscores other classification algorithm traditionally adopted in this field, such as random forests and gradient boosting machines. Finally, [PERSON] et al. [14] have addressed the problem of process outcome prediction in different contexts with a declarative approach that exploits the availability of a formal process model. In summary, previous works in this field have mainly experimented with different classification algorithms and feature encoding techniques to develop more accurate predictive models, rather than focusing on model interpretability. Regarding interpretable (explainable) predictive models in process mining, [PERSON] et al. [15] propose a method to predict the next event in a trace based on dynamic bayesian networks, which are an example of inherently interpretable classification techniques. [PERSON] et al. [16] propose to use gated graph neural networks to predict binary process outcomes by transforming an event log into a graph adjacency matrix and feature maps. The trained models in this case is interpretable because each event is scored for its similarity to others in the training set. Therefore, events with higher score are intended to bear a stronger impact on the outcome. Finally, [PERSON] et al. [17] adopt Shapley value theory to explain factor influence on the outcome of traces predicted using an LSTM model. Regarding the application of association rules in event log analysis, we highlight the work by [PERSON] et al. [18], which applies frequent item-set mining to extract frequent item-sets in an event log that could approximate the relationship among activities normally captured by a process model. Previously, [PERSON] et al. [19] have applied association rule mining to extract rules that are then used to build process models. [PERSON] and [PERSON] [20] propose a trace anomaly detection technique that exploits association rules mined from an event log. The support of association rules, in particular, is used to discriminate between normal and anomalous traces. [PERSON] [21] has proposed a first-order logic-based formalism that is more powerful than traditional process modelling techniques and can be used to specify complex conditions (i.e., rules) regarding the process execution. Association rule mining has already been adapted to create inherently interpretable predictive model in other research field. [PERSON] et al. [22] propose general methods for multi-label classification using association rule mining. They stress, in particular, that association rules highlight common causal relations in the data, which cater for better explainable predictions. In the health care field, [PERSON] et al. [23] adopted fuzzy rule mining combined with causality rank of infrequent adverse drug reaction to distinguish relationship between target drug and adverse event response probability.