Development of a process mining system capable of handling temporal and compositional irregularities

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In this research we developed a method for finding groups of common treatment processes based on the typicalness index, which reflects the global occurrence and transition frequencies of issued orders. We conducted experiments on otolaryngological disease data (158 cases) and observed statistically significant differences on the distributions of applied pathways, among the four clusters generated assessing the similarity of the typicalness indices of order sequences. Similarly, on obstetric disease data (124 cases), we observed statistically significant differences between the distribution of applied pathways and generated clusters. Based on these results, we demonstrated that by using the typicalness measure we could discover the groups of similar treatment processes without giving any a priori information about order types such as operations.