# 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.

## Key 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.