Stable heuristic miner: Applying statistical stability to discover the common patient pathways from location event logs

Araghi, Sina Namaki; Fontanili, Franck; Lamine, Elyes; Okongwu, Uche; Bénaben, Frédérick

doi:10.1016/j.iswa.2022.200071

Cited by 4 publications

(11 citation statements)

References 23 publications

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“…As inferred from our results, experienced teachers seem to leverage several surface cues, such as hand-raising, to infer deep cues, such as interest, thereby constructing more robust reasoning than novice teachers. Within the realm of educational process data mining, tools, such as the Heuristics Miner ( Weijters et al, 2006 ), can be instrumental in discerning the most prevalent paths or sequences of cues and identifying outliers in teachers’ reasoning, while considering the chronology of cue utilization using Petri-Nets and hidden Markov models ( Namaki Araghi et al, 2022 ). In addition, researchers could implement more complex mediator models in their analyses to explore in detail how different behavioral cues are statistically relevant in predicting or mediating teachers’ accuracy of judgment.…”

Section: Discussionmentioning

confidence: 99%

Where experience makes a difference: teachers’ judgment accuracy and diagnostic reasoning regarding student learning characteristics

Kosel,

Bauer,

Seidel

2024

Front. Psychol.

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The concept of teacher professional vision suggests that experienced teachers, compared to novice teachers, might be better at making accurate judgments of students’ learning characteristics, which can be explained by their advanced reasoning in diagnostic situations. This study examines experienced and novice teachers’ diagnoses of different student characteristic profiles: three inconsistent profiles (overestimating, uninterested, and underestimating) and two consistent profiles (strong and struggling). We examined both experienced (n = 19 in-service mathematics teachers) and novice teachers (n = 24 pre-service mathematics teachers) to determine the extent of differences in their judgment accuracy and their diagnostic reasoning about observable cues when diagnosing student profiles while watching a lesson video. ANOVA results indicate that experienced teachers generally achieved a higher judgment accuracy in diagnosing student profiles compared to novice teachers. Moreover, epistemic network analysis of observable cues in experienced and novice teachers’ diagnostic reasoning showed that, compared to novice teachers, experienced teachers make more relations between a broader spectrum of both surface cues (e.g., a student’s hand-raising behavior) and deep cues (e.g., a student being interested in the subject). Experienced teachers thereby construct more comprehensive and robust reasoning compared to novice teachers. The findings highlight how professional experience shapes teachers’ professional skills, such as diagnosing, and suggest strategies for enhancing teacher training.

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Section: Discussionmentioning

confidence: 99%

Where experience makes a difference: teachers’ judgment accuracy and diagnostic reasoning regarding student learning characteristics

Kosel,

Bauer,

Seidel

2024

Front. Psychol.

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“…These thresholds allow modification of the graph by adding or Fig. 1 Illustration of the steps of the first version of the stable heuristic miner to evaluate the statistical stability for activities [10,12] Fig. 2 Stable heuristic miner 2 algorithm: the sequence of actions that lead to the output of the algorithm removing activities (vertices) and connections (edges) among them.…”

Section: The Scientific Gap and Its Risksmentioning

confidence: 99%

“…The primary version of the stable heuristic miner algorithm was proposed to address this research question [10][11][12]. To do so, the stable heuristic miner proposed to evaluate the statistical stability in an event log.…”

Section: The Scientific Gap and Its Risksmentioning

confidence: 99%

Stable Heuristic Miner 2: Evaluating the Statistical Stability in Event Logs to Discover Business Processes

Namaki Araghi,

Fontanili,

Lamine

et al. 2024

Hum-Cent Intell Syst

Self Cite

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Automated process discovery as one of the paradigms of process mining has attracted both industries and academic researchers. These methods offer visibility and comprehension out of complex and unstructured event logs. Over the past decade, the classic heuristic miner and applied heuristic-based process discovery algorithms showed promising results in revealing the hidden process patterns in information systems. One of the challenges related to such algorithms is the arbitrary selection of recorded behaviors in an event log. The offered filtering thresholds are manually adjustable, which could lead to the extraction of a non-optimal process model. This is also visible in commercial process mining solutions. Recently, the first version of the stable heuristic miner algorithm targeted this issue by evaluating the statistical stability of an event log. However, the previous version was limited to evaluating only activities’ behaviors. In this article, we’ll be evaluating the statistical stability of both activities and edges of a graph, which could be discovered from an event log. As a contribution, the stable heuristic miner 2 is introduced. Consequently, the definition of the descriptive reference process model has improved. The novel algorithm is evaluated by using two real-world event logs. These event logs are the familiar Sepsis data set and the urology department patients’ pathways event log, which is recorded by monitoring the interpreted location data of patients on hospital premises and is shared with the scientific community in this article.

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“…This is labeled as RQ2 in Figure 1. To achieve this, we leveraged the stable heuristic miner algorithm [8]. There are other algorithms to discover the reference process model [26,27]; however, to our humble knowledge, they have not been tested using location event logs.…”

Section: Related Workmentioning

confidence: 99%

“…As outlined in Section 2.3, prior to diagnosing a cause, it is necessary to detect any drifts or deviations in the patients' pathways. To achieve this, the DIAG algorithm is based on the stable heuristic miner algorithm [8], and it extends the previous capability of the algorithm to take into account the domain knowledge while discovering process models. Once this step is completed, the DIAG algorithm matches the discovered drifts with PACs modeled by the domain experts in R.IO-DIAG.…”

Section: Diag Algorithmmentioning

confidence: 99%

DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts

Namaki Araghi,

Fontanili,

Sarkar

et al. 2023

Modelling

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The remarkable growth of process mining applications in care pathway monitoring is undeniable. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes constitute an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. Therefore, we mitigate the issue by introducing cognitive process mining through the DIAG approach, which consists of a meta-model and an algorithm. This approach enables reasoning and diagnosing in process mining through an ontology-driven framework. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of our approach.

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Stable heuristic miner: Applying statistical stability to discover the common patient pathways from location event logs

Cited by 4 publications

References 23 publications

Where experience makes a difference: teachers’ judgment accuracy and diagnostic reasoning regarding student learning characteristics

Where experience makes a difference: teachers’ judgment accuracy and diagnostic reasoning regarding student learning characteristics

Stable Heuristic Miner 2: Evaluating the Statistical Stability in Event Logs to Discover Business Processes

DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts

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