Process Mining and Machine Learning for Predicting Clinical Outcomes in Emergency Care: A Study on the MIMICEL Dataset

Antonella Madau, Gianfranco Semeraro

2025

Abstract

The digitization of organizations and the increasing availability of data generated by Information Systems (IS) have led to the development of advanced techniques for business process improvement. Process Mining has emerged as a key discipline bridging the gap between Data Science and Business Process Management (BPM). In this study, we explore the application of classification techniques on the MIMIC-IV-ED dataset, which records patient-level event logs during their stay in the emergency department. The proposed approach starts with process mining to uncover underlying care pathways, followed by thorough data pre-processing and cleaning to construct a structured dataset suitable for classification tasks. In the final stage, we evaluate the performance of seven classification algorithms, encompassing both tree-based and boosting methods, to predict relevant clinical or operational outcomes. Our methodology highlights the synergy between process mining and machine learning, offering insights into patient flow and decision support in emergency care settings.

Paper Citation

in Harvard Style

Madau A. and Semeraro G. (2025). Process Mining and Machine Learning for Predicting Clinical Outcomes in Emergency Care: A Study on the MIMICEL Dataset. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DMDH; ISBN 978-989-758-758-0, SciTePress, pages 791-799. DOI: 10.5220/0013653500003967

in Bibtex Style

@conference{dmdh25,
author={Antonella Madau and Gianfranco Semeraro},
title={Process Mining and Machine Learning for Predicting Clinical Outcomes in Emergency Care: A Study on the MIMICEL Dataset},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DMDH},
year={2025},
pages={791-799},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013653500003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DMDH
TI - Process Mining and Machine Learning for Predicting Clinical Outcomes in Emergency Care: A Study on the MIMICEL Dataset
SN - 978-989-758-758-0
AU - Madau A.
AU - Semeraro G.
PY - 2025
SP - 791
EP - 799
DO - 10.5220/0013653500003967
PB - SciTePress