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

arXiv: https://arxiv.org/abs/2407.02821 · HTML: https://mlsanity.com/paper/2407.02821/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization](https://mlsanity.com/paper/2312.03259/) (2023)
- [Process Mining Model to Predict Mortality in Paralytic Ileus Patients](https://mlsanity.com/paper/2108.01267/) (2021)

## Built on by (most cited first, 6 of 6)

- [Advanced Predictive Modeling for Enhanced Mortality Prediction in ICU Stroke Patients Using Clinical Data](https://mlsanity.com/paper/2407.14211/) (2024)
- [Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database](https://mlsanity.com/paper/2409.01685/) (2024)
- [Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques](https://mlsanity.com/paper/2407.15302/) (2024)
- [Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques](https://mlsanity.com/paper/2408.01144/) (2024)
- [A Semidefinite Relaxation Approach for Fair Graph Clustering](https://mlsanity.com/paper/2410.15233/) (2024)
- [Utilizing Machine Learning Models to Predict Acute Kidney Injury in Septic Patients from MIMIC-III Database](https://mlsanity.com/paper/2412.03737/) (2024)

## Abstract (arXiv)

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.