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Improving heuristics miners for healthcare applications by discovering optimal dependency graphs

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Abstract

Dependency graph discovery is a substantial step in heuristic mining algorithms which are among the most prevalent process discovery methods in the healthcare domain due to their ability to deal with noisy event logs derived from unstructured and highly variable healthcare processes. However, in many healthcare applications, the current dependency graph discovery methods are still likely to create complex (spaghetti-like) and low-quality results. Hence, this paper improves the heuristic mining methods for healthcare applications by introducing a novel integer linear programming model for selecting the optimum set of dependency graph activities and arcs. The proposed method can lead to the extraction of dependency graphs that are simultaneously high-quality and non-complex. According to the assessments, when dealing with artificial and real-life healthcare event logs, the proposed method results in outputs with significantly less complexity and higher quality than the most prominent heuristic mining methods.

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Availability of data and materials

The datasets analyzed during the current study are introduced in Table  4. Rows no. 1 to 10 are available in the 4TU.ResearchData repository, https://data.4tu.nl/categories/_/13611. Rows no. 11 to 13 are available in the processmining.be repository, http://www.processmining.be/fodina/downloads/fodina_experiment_logs.zip.

Code availability

In this study, GAMS and MATLAB were utilized to implement and validate the proposed method. Codes employed in this study can be found in the following repository: https://github.com/MaTavakoli/Healthcare_Dependency_Graph_Discovery.git, in order to allow other researchers to use and draw comparisons in future studies.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Maryam Tavakoli-Zaniani & Mohammad Reza Gholamian

  2. Computer Engineering and Information Technology Department, Amirkabir University of Technology, Tehran, Iran

    Seyyed Alireza Hashemi-Golpayegani

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  1. Maryam Tavakoli-Zaniani
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  2. Mohammad Reza Gholamian
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  3. Seyyed Alireza Hashemi-Golpayegani
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Contributions

MTZhelped in conceptualization, methodology, software, data curation, formal analysis, writing—original draft, writing—review and editing, validation. MRG and SAHG contributed to conceptualization, methodology, supervision, writing—original draft, writing—review and editing.

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Correspondence to Mohammad Reza Gholamian.

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Tavakoli-Zaniani, M., Gholamian, M.R. & Hashemi-Golpayegani, S.A. Improving heuristics miners for healthcare applications by discovering optimal dependency graphs. J Supercomput 78, 19628–19661 (2022). https://doi.org/10.1007/s11227-022-04637-7

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