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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.

References

  1. Battineni G, Chintalapudi N, Amenta F (2020) Model discovery, and replay fitness validation using inductive mining techniques in medical training of CVC surgery. Appl Comput Inf in-print. https://doi.org/10.1016/j.aci.2020.01.001

    Article  Google Scholar 

  2. Garcia CdS et al (2019) Process mining techniques and applications - a systematic mapping study. Expert Syst Appl 133:260–295. https://doi.org/10.1016/j.eswa.2019.05.003

    Article  Google Scholar 

  3. Rojas E, Munoz-Gama J, Sepúlveda M, Capurro D (2016) Process mining in healthcare: a literature review. J Biomed Inf 61:224–236. https://doi.org/10.1016/j.jbi.2016.04.007

    Article  Google Scholar 

  4. Molero-Castillo G, Jasso-Villazul J, Torres-Vargas A, Velázquez-Mena A (2020) Towards the processes discovery in the medical treatment of Mexican-origin women diagnosed with breast cancer. In: Future of information and communication conference. Springer international publishing, Cham, pp 826-838. https://doi.org/10.1007/978-3-030-12388-8_56

  5. Prodel M (2017) Process discovery, analysis and simulation of clinical pathways using health-care data. Dissertation, École des Mines de Saint-Étienne

  6. van der Aalst WMP (2011) Process mining: discovery, conformance and enhancement of business processes. Springer-Verlag, Berlin Heidelberg

    Book  MATH  Google Scholar 

  7. Weijters A, Aalst WMP, Medeiros A (2006) Process mining with the Heuristics miner-algorithm, in BETA Working Paper Series WP 2006, Eindhoven University of Technology

  8. Günther CW, van der Aalst WMP (2007) Fuzzy mining - adaptive process simplification based on multi-perspective metrics. In: International Conference on Business Process Management. Springer-Verlag, Berlin Heidelberg pp 328-343. https://doi.org/10.1007/978-3-540-75183-0_24

  9. Weijters AJMM, Ribeiro JTS (2011) Flexible Heuristics Miner (FHM). In: IEEE symposium on computational intelligence and data mining (CIDM). IEEE, pp 310-317. https://doi.org/10.1109/CIDM.2011.5949453

  10. Burattin A, Sperduti A, Aalst WMP (2012) Heuristics miners for streaming event data. Comput Research Repos. https://doi.org/10.1109/CEC.2014.6900341

    Article  Google Scholar 

  11. Burattin A (2015) Heuristics miner for time interval. In: Burattin A (ed) Process mining techniques in business environments: theoretical aspects, algorithms, techniques and open challenges in process mining. Springer International Publishing, Cham, pp 85-95. https://doi.org/10.1007/978-3-319-17482-2_11

  12. De Cnudde S, Claes J, Poels G (2014) Improving the quality of the heuristics miner in prom 6.2. Expert Syst Appl 41(17):7678–7690

    Article  Google Scholar 

  13. vanden Broucke SKLM, De Weerdt J (2017) Fodina: a robust and flexible heuristic process discovery technique. Decis Support Syst 100:109–118. https://doi.org/10.1016/j.dss.2017.04.005

    Article  Google Scholar 

  14. Mannhardt F, de Leoni M, Reijers HA (2017) Heuristic mining revamped: an interactive, data-aware, and conformance-aware miner. In: Clarisó R, Leopold H, Mendling J, van der Aalst WMP, Kumar A, Pentland B, Weske M (eds) Proceedings of the BPM demo track and BPM dissertation award, co-located with 15th International conference on business process management (BPM 2017), CEUR-WS.org, pp1-5

  15. Mannhardt F, de Leoni M, Reijers HA, van der Aalst WMP (2017) Data-driven process discovery - revealing conditional infrequent behavior from event logs. In: In: Dubois E, Pohl K (eds) Advanced information systems engineering. CAiSE 2017. lecture notes in computer science(), vol 10253. Springer International Publishing, Cham, pp 545-560. https://doi.org/10.1007/978-3-319-59536-8_34

  16. Augusto A, Conforti R, Dumas M, La Rosa M, Bruno G (2018) Automated discovery of structured process models from event logs: the discover-and-structure approach. Data Knowl Eng 117:373–392. https://doi.org/10.1016/j.datak.2018.04.007

    Article  Google Scholar 

  17. Augusto A, Conforti R, Dumas M, La Rosa M, Bruno G (2016) Automated discovery of structured process models: discover structured versus discover and structure. In: Comyn-Wattiau I, Tanaka K, Song I, Yamamoto S, Saeki M (eds) Conceptual Modeling - 35th International Conference, ER 2016. Springer International Publishing, Cham, pp 313–329

    Google Scholar 

  18. Sungkono KR, Rochmah UE, Sarno R (2019) Heuristic linear temporal logic pattern algorithm in business process model. Int J Intell Eng Syst 12(4):31–40

    Google Scholar 

  19. X. Zhou, Y. Tan, G. Zacharewicz, Y. Liu, K. Tan, and D. Chen, “Research on Value Based Heuristics Miner for Product Service System,” in 2021 IEEE 30th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 27-29 Oct. 2021 2021, pp. 89-94, https://doi.org/10.1109/WETICE53228.2021.00028

  20. X. Lu, G. Li, B. Yang, J. Liu, and G. Shan, “StreamFlow: a visual analysis system for 2D streamlines based on workflow mining technique,” Journal of Visualization, vol. 25, no. 2, pp. 307-323, 2022/04/01 2022, https://doi.org/10.1007/s12650-021-00795-7

  21. Yahya BN, Song M, Bae H, Sul S-o, Wu J-Z (2016) Domain-driven actionable process model discovery. Comput Ind Eng 99:382–400. https://doi.org/10.1016/j.cie.2016.05.010

    Article  Google Scholar 

  22. van Zelst SJ, van Dongen BF, van der Aalst WMP, Verbeek HMW (2018) Discovering workflow nets using integer linear programming. Computing 100:529–556. https://doi.org/10.1007/s00607-017-0582-5

    Article  MathSciNet  MATH  Google Scholar 

  23. van der Werf JMEM, van Dongen BF, Hurkens CAJ, Serebrenik (2008) Process discovery using integer linear programming. International Conference on Applications and Theory of Petri Nets. Springer-Verlag, Berlin Heidelberg, pp 368–387. https://doi.org/10.1007/978-3-540-68746-7_24

    Chapter  Google Scholar 

  24. van Zelst SJ, van Dongen BF, van der Aalst WMP (2015) ILP-based process discovery using hybrid regions. In: van der Aalst WMP, Bergenthum R, Carmona J (eds) Algorithms & theories for the analysis of event dData (ATAED’15, Brussels, Belgium, June 22–23, 2015). CEUR-WS.org, Aachen, pp 47–61

    Google Scholar 

  25. Prodel M, Augusto V, Jouaneton B, Lamarsalle L, Xie X (2018) Optimal processm mining for large and complex event logs. IEEE Trans Autom Sci Eng 15:1309–1325. https://doi.org/10.1109/TASE.2017.2784436

    Article  Google Scholar 

  26. Prodel M, Augusto V, Xie X, Jouaneton B, Lamarsalle L (2015) Discovery of patient pathways from a national hospital database using process mining and integer linear programming. In: IEEE International Conference on Automation Science and Engineering (CASE). IEEE, pp 1409-1414. https://doi.org/10.1109/CoASE.2015.7294295

  27. Tavakoli-Zaniani M, Gholamian MR (2022) Improving heuristic process discovery methods through determining the optimal split/join patterns of dependency graphs. IEEE Access 10:1116–1131. https://doi.org/10.1109/ACCESS.2021.3135298

    Article  Google Scholar 

  28. Leemans SJJ, Poppe E, Wynn MT (2019) Directly follows-based process mining: exploration & a casestudy. In: International Conference on Process Mining (ICPM). IEEE, pp 25-32. https://doi.org/10.1109/ICPM.2019.00015

  29. Leemans SJJ, Fahland D (2020) Information-preserving abstractions of event data in process mining. Knowl Inf Syst 62:1143–1197. https://doi.org/10.1007/s10115-019-01376-9

    Article  Google Scholar 

  30. Conforti R, Rosa ML, Hofstede AHMt (2017) Filtering out infrequent behavior from business process event logs. IEEE Trans Knowl Data Eng 29:300–314. https://doi.org/10.1109/TKDE.2016.2614680

    Article  Google Scholar 

  31. Augusto A, Conforti R, Dumas M, La Rosa M, Polyvyanyy A (2019) Split miner: automated discovery of accurate and simple business process models from event logs. Knowl Inf Syst 59:251–284. https://doi.org/10.1007/s10115-018-1214-x

    Article  Google Scholar 

  32. Wirawan NY, Yahya BN, Bae H (2021) Incorporating transaction lifecycle information in blockchain process discovery. Blockchain technology for IoT applications. Springer, Singapore, pp 155–172

    Chapter  Google Scholar 

  33. Pane SF, Awangga RM, Amran Hakim Siregar M, Majesty D (2021) Mapping log data activity using heuristic miner algorithm in manufacture and logistics company. Telkomnika 19(3):781–791

    Article  Google Scholar 

  34. Durojaiye AB, McGeorge NM, Puett LL, Stewart D, Fackler JC, Hoonakker PL, Lehmann HP, Gurses AP (2018) Mapping the flow of pediatric trauma patients using process mining. Appl Clin Inform 9:654–666

    Article  Google Scholar 

  35. Araghi SN, Fontanili F, Lamine E, Salatge N, Benaben F (2020) Interpretation of patients’ location data to support the application of process mining notations. In: Cabitza F, Fred A, Gamboa H (eds) Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (HEALTHINF). Science and Technology Publications, Lda, pp 472-481

  36. Hachicha W, Ghorbel L, Champagnat R, Zayani CA, Amous I (2021) Using process mining for learning resource recommendation: a moodle case study. Procedia Comput Sci 192:853–62

    Article  Google Scholar 

  37. Rahmawati R, Andreswari R, Fauzi R (2022) Analysis and exploratory of lecture preparation process to improve the conformance using process mining. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, pp 0461-0466. https://doi.org/10.1109/CCWC54503.2022.9720762

  38. Chanifah S, Andreswari R, Fauzi R (2021) Analysis of student learning pattern in learning management system (LMS) using heuristic mining a process mining approach. In: 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA). IEEE, pp 121-125. https://doi.org/10.1109/ICERA53111.2021.9538654

  39. Sundari MS, Nayak RK (2021) Efficient tracing and detection of activity deviation in event log using ProM in health care industry. In: 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). IEEE, pp 1238-1245. https://doi.org/10.1109/I-SMAC52330.2021.9640793

  40. Mannhardt FF (2016) Sepsis cases - event log. 4TU.ResearchData. Dataset. https://doi.org/10.4121/uuid:915d2bfb-7e84-49ad-a286-dc35f063a460

  41. Mannhardt F (2017) Hospital billing - event log. 4TU.ResearchData. Dataset. https://doi.org/10.4121/uuid:76c46b83-c930-4798-a1c9-4be94dfeb741

  42. van Dongen B (2011) Real-life event logs - hospital log. 4TU.ResearchData. Dataset. https://doi.org/10.4121/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54

  43. Bose RPJC, van der Aalst WMP (2011) Analysis of patient treatment procedures. In: Daniel F, Barkaoui K, Dustdar S (eds) Business process management workshops. BPM 2011. Lecture notes in business information processing, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28108-2_17

  44. Augusto A, Conforti R, Dumas M, La Rosa M, Bruno G (2018) Automated discovery of structured process models from event logs: The discover-and-structure approach. Data Knowl Eng 117:373–392. https://doi.org/10.1016/j.datak.2018.04.007

    Article  Google Scholar 

  45. Muñoz-Gama J, Carmona J (2010) A Fresh look at precision in process conformance. In: International Conference on Business Process Management. Springer-Verlag, Berlin, Heidelberg, pp 211-226. https://doi.org/10.1007/978-3-642-15618-2_16

  46. Nemhauser G, Wolsey L (1999) Computational complexity. In: Integer and combinatorial optimization. John Wiley & Sons, pp 114-145

  47. Carmona J, van Dongen B, Solti A, Weidlich M (2018) Quality dimensions for relating processes and models. In: Conformance checking. Springer, Cham, pp 43-61. https://doi.org/10.1007/978-3-319-99414-7_3

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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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