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Data Lifting for Process-Aware Analysis of Clinical Pathways

Year: 2026

Type: conference-paper

Abstract: This record contains the accepted author manuscript of the paper “Data Lifting for Process-Aware Analysis of Clinical Pathways” presented at the 2026 2nd International Conference on Emerging and Intelligent Technologies and Systems (EITS 2026). The paper proposes a process-aware data-lifting pipeline for transforming heterogeneous clinical data into temporally consistent event logs suitable for predictive monitoring. The approach integrates temporal reconstruction, event abstraction, and deterministic trace construction to support reproducible process-aware predictive analytics in healthcare settings. This version has not undergone copy-editing or typesetting by IEEE. The final authenticated version will be published in the IEEE conference proceedings and will be available via IEEE Xplore and its DOI. © IEEE. Personal use of this material is permitted. This work was supported by the National Recovery and Resilience Plan (NRRP), M4C2I1.1, funded by the European Union – NextGenerationEU – Project Title aRtificial intElligence for Process Analytics (REPA) – Grant Assignment Decree No. 2022CJWPNA by the Italian Ministry of University and Research (MUR).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20058691

Authors: Ardimento, Pasquale, Bernardi, Mario Luca, CIMITILE, Marta, Latorre, Samuele

Institutions: University of Bari Aldo Moro, University of Sannio

Language: English

Topic: Business Process Modeling and Analysis

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7160431609

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