Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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Does your accurate process predictive monitoring model give reliable predictions?

The evaluation of business process predictive monitoring models usually focuses on accuracy of predictions. While accuracy aggregates performance across a set of process cases, in many practical scenarios decision makers are interested in the reliability of an individual prediction, that is, an indication of how likely is a given prediction to be eventually correct. This paper proposes a first definition of business process prediction reliability and shows, through the experimental evaluation, that metrics that include features defining the variability of a process case often give a better prediction reliability indication than metrics that include the probability estimation computed by the machine learning model used to make predictions alone

An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models

A plethora of automated process discovery techniques have been developed which aim to discover a process model based on event data originating from the execution of business processes. The aim of the discovered process models is to describe the control-flow of the underlying business process. At the same time, a variety of sequence modeling techniques have been developed in the machine learning domain, which aim at finding an accurate, not necessarily interpretable, model describing sequence data. Both approaches ultimately aim to find a model that generalizes the behavior observed, i.e., they describe behavior that is likely to be part of the underlying distribution, whilst disallowing unlikely behavior. While the generalizing capabilities of process discovery algorithms have been studied before, a comparison, in terms of generalization, w.r.t. sequence models is not yet explored. In this paper we present an experimental evaluation of the generalizing capabilities of automated process discovery techniques and black-box sequence models, on the basis of next activity prediction. We compare a range of process discovery and sequence modeling techniques on a range of real-life datasets from the business process management domain. Our results indicate that LSTM neural networks more accurately describe previously unseen traces (i.e., test traces) than existing process discovery methods