Conformance checking in healthcare based on partially ordered event data

Lu, Xixi; Mans, Ronny S.; Fahland, Dirk; Aalst, Wil M. P. van der

doi:10.1109/etfa.2014.7005060

Cited by 24 publications

(29 citation statements)

References 16 publications

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“…P-traces can be computed by careful inspection of the event timestamps. The techniques to extract p-traces are extended in [41] in order to deal with data. However, the use of data attributes is narrower compared to the approach presented in this paper: data attributes are split into read/write accesses to data values, and simple rules to extract concurrency and dependency are introduced to take into account the role of a data access within a trace.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Mining Conditional Partial Order Graphs from Event Logs

Mokhov

Carmona

Beaumont

2016

Transactions on Petri Nets and Other Models of Concurrency XI

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Process mining techniques rely on event logs: the extraction of a process model (discovery) takes an event log as the input, the adequacy of a process model (conformance) is checked against an event log, and the enhancement of a process model is performed by using available data in the log. Several notations and formalisms for event log representation have been proposed in the recent years to enable efficient algorithms for the aforementioned process mining problems. In this paper we show how Conditional Partial Order Graphs (CPOGs), a recently introduced formalism for compact representation of families of partial orders, can be used in the process mining field, in particular for addressing the problem of compact and easy-to-comprehend representation of event logs with data. We present algorithms for extracting both the control flow as well as the relevant data parameters from a given event log and show how CPOGs can be used for efficient and effective visualisation of the obtained results. We demonstrate that the resulting representation can be used to reveal the hidden interplay between the control and data flows of a process, thereby opening way for new process mining techniques capable of exploiting this interplay.

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Section: Related Work and Discussionmentioning

confidence: 99%

Mining Conditional Partial Order Graphs from Event Logs

Mokhov

Carmona

Beaumont

2016

Transactions on Petri Nets and Other Models of Concurrency XI

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“…Two papers concerned the analysis of process anomalies and exceptions [33], [34], and some others concerned common pathways [35]- [38]. Problems and challenges of process characteristic issues and event log quality issues were well discussed in three papers [39]- [41].…”

Section: Resultsmentioning

confidence: 99%

“…These were then applied to their case studies. The eleven papers following this general methodology were [22], [27], [30], [32], [34], [35], [41], [47], [48], [58]- [60].…”

Section: E Methodologiesmentioning

confidence: 99%

“…Some papers identified data improvements [28]- [30], [36]- [38], [48], [57], [64] in terms of improving data quality, dimensionality, and complexity. A larger number of important technical improvements were identified in 20 papers [3], [21]- [23], [29]- [31], [35], [39]- [41], [44], [48]- [52], [58], [60], [64]. These were to:…”

Section: F Limitations and Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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Abstract-Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.

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“…Numerous applications of process mining to real-life event logs illustrate that concurrency is a key notion in process discovery [1,7,8]. One should avoid showing all observed interleavings in a process model.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Frequent Episodes in Event Logs

Leemans

Aalst

2015

Lecture Notes in Business Information Processing

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Abstract. Lion's share of process mining research focuses on the discovery of end-to-end process models describing the characteristic behavior of observed cases. The notion of a process instance (i.e., the case) plays an important role in process mining. Pattern mining techniques (such as frequent itemset mining, association rule learning, sequence mining, and traditional episode mining) do not consider process instances. An episode is a collection of partially ordered events. In this paper, we present a new technique (and corresponding implementation) that discovers frequently occurring episodes in event logs thereby exploiting the fact that events are associated with cases. Hence, the work can be positioned in-between process mining and pattern mining. Episode discovery has its applications in, amongst others, discovering local patterns in complex processes and conformance checking based on partial orders. We also discover episode rules to predict behavior and discover correlated behaviors in processes. We have developed a ProM plug-in that exploits efficient algorithms for the discovery of frequent episodes and episode rules. Experimental results based on real-life event logs demonstrate the feasibility and usefulness of the approach.

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Conformance checking in healthcare based on partially ordered event data

Cited by 24 publications

References 16 publications

Mining Conditional Partial Order Graphs from Event Logs

Mining Conditional Partial Order Graphs from Event Logs

Process mining in oncology: A literature review

Discovery of Frequent Episodes in Event Logs

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