Process Mining for Clinical Processes

Partington, Andrew; Wynn, Moe Thandar; Suriadi, Suriadi; Ouyang, Chun; Karnon, Jonathan

doi:10.1145/2629446

Cited by 113 publications

(45 citation statements)

References 29 publications

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“…In our case study, we demonstrate how both process mining analysis, with its proven ability to extract detailed behavioural insights from fine-grained and chronologically-arranged data, as evidenced by the insights obtained from previous process mining case studies [7], [8], [9], [15], and confirmatory statistics, can be weaved together with clustering analysis to not only understand the variety of behaviours exhibited by gamblers, but also to evaluate the results.…”

Section: Introductionmentioning

confidence: 90%

See 1 more Smart Citation

Using Data-Driven and Process Mining Techniques for Identifying and Characterizing Problem Gamblers in New Zealand

Suriadi

Sušnjak

Ponder-Sutton

et al. 2016

CSIMQ

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This article uses data-driven techniques combined with established theory in order to analyse gambling behavioural patterns of 91 thousand individuals on a real-world fixed-odds gambling dataset in New Zealand. This research uniquely integrates a mixture of process mining, data mining and confirmatory statistical techniques in order to categorise different subgroups of gamblers, with the explicit motivation of identifying problem gambling behaviours and reporting on the challenges and lessons learned from our case study. We demonstrate how techniques from various disciplines can be combined in order to gain insight into the behavioural patterns exhibited by different types of gamblers, as well as provide assurances of the correctness of our approach and findings. A highlight of this case study is both the methodology which demonstrates how such a combination of techniques provides a rich set of effective tools to undertake an exploratory and open-ended data analysis project that is guided by the process cube concept, as well as the findings themselves which indicate that the contribution that problem gamblers make to the total volume, expenditure, and revenue is higher than previous studies have maintained.

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Section: Introductionmentioning

confidence: 90%

“…However, existing cross-organisational process mining case studies (which could benefit most from such hypothesis testing), e.g. [10], [15], have not done so.…”

Section: Introductionmentioning

confidence: 99%

Using Data-Driven and Process Mining Techniques for Identifying and Characterizing Problem Gamblers in New Zealand

Suriadi

Sušnjak

Ponder-Sutton

et al. 2016

CSIMQ

Self Cite

View full text Add to dashboard Cite

show abstract

“…The output model can be in the form of a Petri net, a transition graph, or a Business Process Model and Notation graph. Process mining has shown significant benefit in understanding underlying task flows, bottlenecks, resource utilisation and many other factors within large corporations (Van Der Aalst et al 2007; van der Aalst 2013), and also proved beneficial in healthcare (Mans et al 2008;Partington et al 2015;Rojas et al 2016) to learn and improve the underlying process.…”

Section: Process Miningmentioning

confidence: 99%

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Farooqui

Bengtsson

Falkman

et al. 2020

International Journal of Production Research

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The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry 4.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner -a tool for modelling and analysing production systems -and is currently implemented at an automotive company as a pilot project.

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“…In the information systems context, this is referred to workflow mining, aiming at retrieving business process models from the analysis of event logs recorded in one or more information systems used to support those processes. Quite a lot of research has been done in this setting ( [12,5,24] to name just a few). The reached results focused on different problems, such as log analysis through clustering, data cleaning from noise, or recognition of particular workflow patterns.…”

Section: Related Workmentioning

confidence: 99%

Model Checking Based Approach for Compliance Checking

Martinelli

Mercaldo

Nardone

et al. 2019

ITC

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Process mining is the set of techniques to retrieve a process model starting from available logging data. The discovered process model has to be analyzed to verify it respects the defined properties, i.e., the so-called compliance checking. Our aim is to use a model checking based approach to verify compliance. First, we propose an integrated-tool approach using existing tools as ProM (a framework supporting process mining techniques) and CADP (a formal verification environment). More precisely, the execution traces from a software system are extracted. Then, using the “Mine Transition System” plugin in ProM, we obtain a labelled transition system, that can be easily used to verify formal properties trough CADP. However, this choice presents the “state explosion” problem, i.e., models discovered through the classical process mining techniques tend to be large and complex. In order to solve this problem, another custom-made approach is shown, which accomplishes a pre- processing on the traces to obtain abstract traces, where abstraction is based on the set of temporal logic formulae specifying the system properties. Then, from the set of abstracted traces, we discover a system described in Lotos, a process algebra specification language; in this way we do not build an operational model for the system, but we produce only a language description from which a model checking environment will automatically obtain the reduced corresponding transition system. Real systems have been used as case studies to evaluate the proposed methodologies.

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Process Mining for Clinical Processes

Cited by 113 publications

References 29 publications

Using Data-Driven and Process Mining Techniques for Identifying and Characterizing Problem Gamblers in New Zealand

Using Data-Driven and Process Mining Techniques for Identifying and Characterizing Problem Gamblers in New Zealand

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Model Checking Based Approach for Compliance Checking

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