Authors:
Antonella Madau
1
and
Gianfranco Semeraro
2
Affiliations:
1
Department of Engineering, University of Sannio, Benevento, Italy
;
2
Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
Keyword(s):
Process Mining, Healthcare, Machine Learning, MIMICEL, Emergency Departments.
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 i
nsights into patient flow and decision support in emergency care settings.
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