Effect of a Process Mining based Pre-processing Step in Prediction of the Critical Health Outcomes

TL;DR

This paper demonstrates that a process mining based pre-processing step, specifically concatenation, enhances data quality, process model accuracy, and prediction performance of critical health outcomes in healthcare datasets.

Contribution

The study introduces the use of concatenation as a pre-processing step to improve process model quality and outcome prediction accuracy in healthcare data analysis.

Findings

Concatenation improved process model metrics such as fitness, precision, and F-Measure.

Pre-processing increased the accuracy of critical health outcome predictions.

Process model complexity was reduced after applying concatenation.

Abstract

Predicting critical health outcomes such as patient mortality and hospital readmission is essential for improving survivability. However, healthcare datasets have many concurrences that create complexities, leading to poor predictions. Consequently, pre-processing the data is crucial to improve its quality. In this study, we use an existing pre-processing algorithm, concatenation, to improve data quality by decreasing the complexity of datasets. Sixteen healthcare datasets were extracted from two databases - MIMIC III and University of Illinois Hospital - converted to the event logs, they were then fed into the concatenation algorithm. The pre-processed event logs were then fed to the Split Miner (SM) algorithm to produce a process model. Process model quality was evaluated before and after concatenation using the following metrics: fitness, precision, F-Measure, and complexity. The pre-processed event logs were also used as inputs to the Decay Replay Mining (DREAM) algorithm to predict critical outcomes. We compared predicted results before and after applying the concatenation algorithm using Area Under the Curve (AUC) and Confidence Intervals (CI). Results indicated that the concatenation algorithm improved the quality of the process models and predictions of the critical health outcomes.