Process-oriented Iterative Multiple Alignment for Medical Process Mining

TL;DR

This paper introduces PIMA, a new process-oriented iterative alignment method that improves the quality and efficiency of medical workflow data analysis compared to existing techniques.

Contribution

PIMA is a novel iterative alignment framework that achieves better alignment quality and faster computation for large medical process datasets.

Findings

PIMA outperforms existing algorithms in sum-of-pairs score.

PIMA reduces computational complexity from O(N^2L^2) to O(NL^2).

PIMA enhances data visualization and insight extraction in medical workflows.

Abstract

Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.