Abstract
Clinical pathways are highly variable and although many patients may follow similar pathway each individual will experience a unique set of events, for example with multiple repeated activities or varied sequences of activities. Process mining techniques are able to discover generalizable pathways based on data mining of event logs but using process mining techniques on a raw clinical pathway data to discover underlying healthcare processes is challenging due to this high variability. This paper involves two main contributions to healthcare process mining. The first contribution is developing a novel approach for event selection and outlier removing in order to improve pattern detection and thus representational quality. The second contribution is to demonstrate a new open access medical dataset, the MIMIC-III (Medical Information Mart for Intensive Care) database, which has not been used in process mining publications.
In this paper, we developed a new method for variations reduction in clinical pathways data. Variation can result from outlier events that prevent capturing clear patterns. Our approach targets the behavior of repeated activities. It uses interval-based patterns to determine outlier threshold based on the time of events occurring and the distinctive attribute of observed events.
The approach is tested on clinical pathways data for diabetes patients with congestive heart failure extracted from the MIMIC-III medical database and analyzed using the ProM process mining tool. The method has improved model precision conformance without reducing model fitness. We were able to reduce the number of events while making sure the mainstream patterns were unaffected. We found that some activity types had a large number of outlier events whereas other activities had a relatively few. The interval-based event selection method has the potential of improve process visualization. This approach is undergoing implementation as an event log enhancement technique in the ProM tool.
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References
Van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)
Van Dongen, B.F., Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005). doi:10.1007/11494744_25
Mans, R.S., Van der Aalst, W.M.P., Vanwersch, R.J.B.: Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes. Springer, Heidelberg (2015)
Weiskopf, N.G., Weng, C.: Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J. Am. Med. Inform. Assoc. 20(1), 144–151 (2013)
Van der Aalst, W., Adriansyah, A., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). doi:10.1007/978-3-642-28108-2_19
Bose, R.J.C., Mans, R.S., Van der Aalst, W.: Wanna improve process mining results? In: 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE (2013)
de San Pedro, J., Cortadella, J.: Discovering duplicate tasks in transition systems for the simplification of process models. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 108–124. Springer, Cham (2016). doi:10.1007/978-3-319-45348-4_7
Vázquez-Barreiros, B., Mucientes, M., Lama, M.: Mining duplicate tasks from discovered processes. In: ATAED@PetriNets/ACSD (2015)
Van der Aalst, W., et al.: Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9(1), 87 (2010)
Broucke, S.V.: Advances in process mining: artificial negative events and other techniques (2014)
da Silva, L.F.N.: Process mining: application to a case study (2014)
Lu, X., Fahland, D., Biggelaar, F.J.H.M., Aalst, W.M.P.: Handling duplicated tasks in process discovery by refining event labels. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 90–107. Springer, Cham (2016). doi:10.1007/978-3-319-45348-4_6
Suriadi, S., et al.: Event log imperfection patterns for process mining: towards a systematic approach to cleaning event logs. Inf. Syst. 64, 132–150 (2017)
Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci Data 3, 160035 (2016)
MIMIC medical database. MIMIC-III critical care database (2015). https://mimic.physionet.org/gettingstarted/access/. Accessed 9 Mar 2017
Kurniati, A., et al.: The assessment of data quality issues for process mining in healthcare using MIMIC-III, a publicly available e-health record database (2017)
Adriansyah, A., et al.: Measuring precision of modeled behavior. IseB 13(1), 37–67 (2015)
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Alharbi, A., Bulpitt, A., Johnson, O. (2017). Improving Pattern Detection in Healthcare Process Mining Using an Interval-Based Event Selection Method. In: Carmona, J., Engels, G., Kumar, A. (eds) Business Process Management Forum. BPM 2017. Lecture Notes in Business Information Processing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-65015-9_6
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DOI: https://doi.org/10.1007/978-3-319-65015-9_6
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