## 2 Use Case Description  
Context and ScopeIn our study, we focus on a process mining initiative that was conducted by [16] at a regional hospital in the Netherlands, a facility with approximately 700 beds across multiple locations and an annual patient intake of around 50,000 individuals. An event log was constructed through SQL-based extraction, anonymized, and archived in the 4 TU. Center for Research Data repository [17]. Process mining--a data-driven methodology for analyzing workflows via event logs enables here to address the complexity of emergency care pathways, with a focus on sepsis management. By leveraging process mining, our study specifically aims to predictively analyze trajectories leading to Admission to the Intensive Care Unit (ICU), a high-stakes transition reflecting escalating care needs and systemic instability. Sepsis, a leading cause of ICU admissions and in-hospital mortality, was selected due to its standardized diagnostic criteria (Systemic Inflammatory Response Syndrome, SIRS) and time-sensitive treatment protocols. Data Collection and IntegrationData spanning 1.5 years (November 2013-June 2015) encompass 1050 sepsis patient cases, yielding 15,214 timestamped events. These were extracted from three heterogeneous source systems: triage documentation, laboratory systems, and financial/administrative databases (see Figure 1). Triage records provide granular details such as symptom checklists, diagnostic order timestamps, and administration times for intravenous antibiotics and fluids. Laboratory data include blood test results for leukocytes, C-reactive protein (CRP), and lactic acid, while financial and administrative systems track admissions, care transitions (e.g., transfers to intensive care), discharges, and post-treatment trajectories. Process mining's strength in reconciling multi-source data proved critical here, resolving inconsistencies (e.g., timestamp alignment, unit conversions) to reconstruct temporally coherent patient pathways. This structured event log enables the identification of patterns preceding ICU transfers, such as delayed antibiotic administration or abnormal vital signs, which are often obscured in siloed healthcare datasets. Event Log Structure and AttributesThe final event log comprised 16 activity classes organized into clinically meaningful categories: registration/triage (e.g., ER Sepsis Triage), diagnostic procedures (leukocyte, CRP, and lactic acid measurements), treatment interventions (IV administration), care transitions (admissions, transfers), discharge processes (five variants), and critical escalation events (ICU admissions). Each event was enriched  
Figure 1: The consolidation process of data from multiple source systems into a single event log as described by [16]. with 28 attributes, including patient age, anonymized timestamps (preserving inter-event durations), blood test values, diagnostic findings (e.g., organ dysfunction), and logistical metadata such as care team assignments and clinical flags (e.g., hypotension, hypoxia). Table 1 provides an illustrative excerpt, demonstrating the log's granularity. For instance, a single case (ID: B) spans registration, triage, diagnostic tests, and sepsis-specific interventions, with CRP values indicating severe inflammation (240.0 mg/L). The dataset captured 890 unique process variants, reflecting diverse pathways such as direct ICU admissions, delayed transfers after initial stabilization, and discharges without escalation--key insights for understanding risk stratification in sepsis care. _Focus of Our Research._ In this work, we focus on forecasting Admission to ICU, a critical juncture in sepsis care where timely intervention can significantly influence patient outcomes.