A multi-objective medical process mining model using event log and causal matrix

Hejazi, Seyed Mojtaba; Zandieh, M.; Mirmozaffari, Mirpouya

doi:10.1016/j.health.2023.100188

Cited by 4 publications

(2 citation statements)

References 64 publications

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“…One of the objectives of process mining is to automatically create a process model that depicts the activities performed in the event logs. This process model illustrates how a process should be conducted based on the recorded data of its execution [10].…”

Section: Methodsmentioning

confidence: 99%

Process Mining on New Student Admission Process in Telkom University using Genetic Miner

Yogi,

Angelina Prima Kurniati,

Ichwanul Muslim Karo Karo

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The selection process for new students at Telkom University, also known as SMB Telkom University has been running for years and already has its process flow. However, the existing process flow can be further improved to better reflect the actual field processes and become more accurate. Process mining can enhance this process flow by creating a new process flow based on event logs or previously executed processes. One of the algorithms in process mining is genetic process mining, where process mining is performed multiple times over several generations and genetic algorithms such as crossover and mutation are applied to generate a more accurate process model compared to other process mining algorithms such as heuristic and inductive mining. After conducting experiments, the best process model that was produced was at the 100th generation which has a fitness point of 0.755910819 and precision point of 0.742857143, after examining the parameters and the resulting Petri net or process flow that was produced it was concluded that the process model obtained from the application of Genetic Process Mining to SMB Telkom University is not very good because the resulting Petri net has several duplicate activities and appears to be non-linear. This could be due to several factors i.e., incompatible, or inaccurate data.

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Section: Methodsmentioning

confidence: 99%

Process Mining on New Student Admission Process in Telkom University using Genetic Miner

Yogi,

Angelina Prima Kurniati,

Ichwanul Muslim Karo Karo

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Many research papers have explored basic DEA models for public hospitals and acute care centers [ 39 , 40 , 41 , 42 , 43 ]. However, in this narrative review, our focus is only on articles applying basic DEA models for EDs, which are discussed below.…”

Section: Techniques and Viewsmentioning

confidence: 99%

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

Mirmozaffari,

Kamal

2023

Healthcare

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The healthcare industry is one application for data envelopment analysis (DEA) that can have significant benefits for standardizing health service delivery. This narrative review focuses on the application of DEA in emergency departments (EDs) and the management of emergency conditions such as acute ischemic stroke and acute myocardial infarction (AMI). This includes benchmarking the proportion of patients that receive treatment for these emergency conditions. The most frequent primary areas of study motivating work in DEA, EDs and management of emergency conditions including acute management of stroke are sorted into five distinct clusters in this study: (1) using basic DEA models for efficiency analysis in EDs, i.e., applying variable return to scale (VRS), or constant return to scale (CRS) to ED operations; (2) combining advanced and basic DEA approaches in EDs, i.e., applying super-efficiency with basic DEA or advanced DEA approaches such as additive model (ADD) and slack-based measurement (SBM) to clarify the dynamic aspects of ED efficiency throughout the duration of a first-aid program for AMI or heart attack; (3) applying DEA time series models in EDs like the early use of thrombolysis and percutaneous coronary intervention (PCI) in AMI treatment, and endovascular thrombectomy (EVT) in acute ischemic stroke treatment, i.e., using window analysis and Malmquist productivity index (MPI) to benchmark the performance of EDs over time; (4) integrating other approaches with DEA in EDs, i.e., combining simulations, machine learning (ML), multi-criteria decision analysis (MCDM) by DEA to reduce patient waiting times, and futile transfers; and (5) applying various DEA models for the management of acute ischemic stroke, i.e., using DEA to increase the number of eligible acute ischemic stroke patients receiving EVT and other medical ischemic stroke treatment in the form of thrombolysis (alteplase and now Tenecteplase). We thoroughly assess the methodological basis of the papers, offering detailed explanations regarding the applied models, selected inputs and outputs, and all relevant methodologies. In conclusion, we explore several ways to enhance DEA’s status, transforming it from a mere technical application into a strong methodology that can be utilized by healthcare managers and decision-makers.

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Multiobjective Binary Differential Approach with Parameter Tuning for Discovering Business Process Models: MoD‐ProM

Deshmukh,

Gupta,

Kumar

2024

The Scientific World Journal

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Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions, completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multiobjective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multiobjective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using grey relational analysis combined with the Taguchi approach. We have compared the proposed approach with the well‐known single‐objective algorithms and state‐of‐the‐art multiobjective evolutionary algorithm—Nondominated Sorting Genetic Algorithm (NSGA‐II). Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state‐of‐the‐art algorithms.

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A multi-objective medical process mining model using event log and causal matrix

Cited by 4 publications

References 64 publications

Process Mining on New Student Admission Process in Telkom University using Genetic Miner

Process Mining on New Student Admission Process in Telkom University using Genetic Miner

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

Multiobjective Binary Differential Approach with Parameter Tuning for Discovering Business Process Models: MoD‐ProM

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