Performance-preserving event log sampling for predictive monitoring

Abstract

Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy.

A Deep Learning Approach for Predictive Healthcare Process Monitoring

“…The use of LSTM neural networks has been employed in diverse fields of healthcare, e.g., predicting healthcare trajectories from medical records [35], analyzing longitudinal patient records [36], predicting patient spending on medications [37], and predicting an initial diagnosis of heart failure [38]. Regarding experiment 2, in [39], they proposed a method based on LSTM using the event log sepsis to predict the next activity, obtaining a value of 0.60 in the F1-score metric. In [40], it compares the convolutional neural network (CNN) and an LSTM for predicting the next event, reaching a score of 0.57 in the ROC-AUC and 0.84 in the accuracy metrics.…”