6 research outputs found
Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles
Recent papers have introduced a novel approach to explain why a Predictive
Process Monitoring (PPM) model for outcome-oriented predictions provides wrong
predictions. Moreover, they have shown how to exploit the explanations,
obtained using state-of-the art post-hoc explainers, to identify the most
common features that induce a predictor to make mistakes in a semi-automated
way, and, in turn, to reduce the impact of those features and increase the
accuracy of the predictive model. This work starts from the assumption that
frequent control flow patterns in event logs may represent important features
that characterize, and therefore explain, a certain prediction. Therefore, in
this paper, we (i) employ a novel encoding able to leverage DECLARE constraints
in Predictive Process Monitoring and compare the effectiveness of this encoding
with Predictive Process Monitoring state-of-the art encodings, in particular
for the task of outcome-oriented predictions; (ii) introduce a completely
automated pipeline for the identification of the most common features inducing
a predictor to make mistakes; and (iii) show the effectiveness of the proposed
pipeline in increasing the accuracy of the predictive model by validating it on
different real-life datasets
Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns
Prescriptive Process Monitoring systems recommend, during the execution of a
business process, interventions that, if followed, prevent a negative outcome
of the process. Such interventions have to be reliable, that is, they have to
guarantee the achievement of the desired outcome or performance, and they have
to be flexible, that is, they have to avoid overturning the normal process
execution or forcing the execution of a given activity. Most of the existing
Prescriptive Process Monitoring solutions, however, while performing well in
terms of recommendation reliability, provide the users with very specific
(sequences of) activities that have to be executed without caring about the
feasibility of these recommendations. In order to face this issue, we propose a
new Outcome-Oriented Prescriptive Process Monitoring system recommending
temporal relations between activities that have to be guaranteed during the
process execution in order to achieve a desired outcome. This softens the
mandatory execution of an activity at a given point in time, thus leaving more
freedom to the user in deciding the interventions to put in place. Our approach
defines these temporal relations with Linear Temporal Logic over finite traces
patterns that are used as features to describe the historical process data
recorded in an event log by the information systems supporting the execution of
the process. Such encoded log is used to train a Machine Learning classifier to
learn a mapping between the temporal patterns and the outcome of a process
execution. The classifier is then queried at runtime to return as
recommendations the most salient temporal patterns to be satisfied to maximize
the likelihood of a certain outcome for an input ongoing process execution. The
proposed system is assessed using a pool of 22 real-life event logs that have
already been used as a benchmark in the Process Mining community.Comment: 38 pages, 6 figures, 8 table
Nirdizati 2.0: New Features and Redesigned Backend
Nirdizati is a dedicated tool for Predictive Process Monitoring, a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. Nirdizati is a web application supporting users in building, comparing, and analyzing predictive models that can then be used to perform predictions on the future of an ongoing case. By providing a rich set of different state-of-the-art approaches, Nirdizati offers BPM researchers and practitioners a useful and flexible instrument for investigating and comparing Predictive Process Monitoring techniques. In this paper, we present a Nirdizati version with a redesigned backend, which improves its modularity and scalability, and with new features, which further enrich its capability to support researchers and practitioners to deal with different monitoring tasks
Process Mining Handbook
This is an open access book. This book comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. This volume contains 17 chapters organized into the following topical sections: Introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Research Paper: Process Mining and Synthetic Health Data: Reflections and Lessons Learnt
Analysing the treatment pathways in real-world health data can provide valuable insight for clinicians and decision-makers. However, the procedures for acquiring real-world data for research can be restrictive, time-consuming and risks disclosing identifiable information. Synthetic data might enable representative analysis without direct access to sensitive data. In the first part of our paper, we propose an approach for grading synthetic data for process analysis based on its fidelity to relationships found in real-world data. In the second part, we apply our grading approach by assessing cancer patient pathways in a synthetic healthcare dataset (The Simulacrum provided by the English National Cancer Registration and Analysis Service) using process mining. Visualisations of the patient pathways within the synthetic data appear plausible, showing relationships between events confirmed in the underlying non-synthetic data. Data quality issues are also present within the synthetic data which reflect real-world problems and artefacts from the synthetic dataset’s creation. Process mining of synthetic data in healthcare is an emerging field with novel challenges. We conclude that researchers should be aware of the risks when extrapolating results produced from research on synthetic data to real-world scenarios and assess findings with analysts who are able to view the underlying data