Evaluation of Trace Alignment Quality and its Application in Medical Process Mining

TL;DR

This paper compares and improves reference-free evaluation methods for trace alignment in process mining, introducing a global assessment approach and a new complexity metric, with applications in medical process analysis.

Contribution

It presents enhanced reference-free evaluation methods for trace alignment, including a global assessment and a novel complexity metric, improving overall alignment quality measurement.

Findings

Improved global evaluation method for trace alignment.

Introduced a new metric for alignment complexity.

Validated methods on trauma resuscitation data.

Abstract

Trace alignment algorithms have been used in process mining for discovering the consensus treatment procedures and process deviations. Different alignment algorithms, however, may produce very different results. No widely-adopted method exists for evaluating the results of trace alignment. Existing reference-free evaluation methods cannot adequately and comprehensively assess the alignment quality. We analyzed and compared the existing evaluation methods, identifying their limitations, and introduced improvements in two reference-free evaluation methods. Our approach assesses the alignment result globally instead of locally, and therefore helps the algorithm to optimize overall alignment quality. We also introduced a novel metric to measure the alignment complexity, which can be used as a constraint on alignment algorithm optimization. We tested our evaluation methods on a trauma resuscitation dataset and provided the medical explanation of the activities and patterns identified as deviations using our proposed evaluation methods.