## 1 Introduction  
The rapid identification of clinical deterioration in patients presenting to the emergency department (ED) is crucial to prevent severe adverse events, such as unplanned intensive care unit (ICU) admissions, cardiac arrest, and in-hospital death [1]. To address this issue, Early Warning Scores (EWSs) were developed to systematically monitor vital signs and detect early clinical deterioration [2, 3]. Among these tools, the National Early WarningScore (NEWS) is one of the most widely adopted and validated systems in both hospital and pre-hospital settings [4; 5; 6]. The main objective of the NEWS is to enable early recognition of patients at high risk of deterioration, allowing for timely intervention and prevention of adverse outcomes (Table 1) [4; 7]. In recent years, numerous studies have explored the use of the NEWS within EDs [8; 9; 10; 11; 12; 13]. Some studies have focused on specific patient subgroups with clinical conditions, such as sepsis or chronic respiratory failure, while others have examined the application of the NEWS across different stages of patient care in the ED, including triage or patient boarding [9; 13; 14; 15]. Additional studies have analyzed the NEWS in combination with biomarkers, reporting results with potential clinical relevance [11; 16]. The need to study and validate the NEWS specifically for the ED context arises from the unique and diverse patient population encountered in this setting, which differs significantly from the inpatient wards for which the NEWS was originally created and validated [12]. Indeed, patients presenting to the ED exhibit not only a wide heterogeneity of clinical presentations but also varying levels of acuity [17]. Moreover, in many ED patients, the underlying pathophysiological process is often in its early stages, meaning significant alterations in vital parameters may not yet be evident [18]. Furthermore, alterations in vital parameters may arise from non-pathological processes (e.g., anxiety states), while, conversely, there may be an apparent stability in parameters masking an already ongoing pathological process with a high risk of rapid deterioration (e.g., sepsis) [19; 20; 21]. Therefore, it is essential to evaluate the role of parameters included in the NEWS and assess their applicability in the heterogeneous context of the ED. Despite its widespread use, there is limited evidence on the optimization of the NEWS specifically for the ED population. In particular, no studies have evaluated the contribution of individual NEWS parameters using machine learning approaches, specifically, decision trees analysis. A recent study conducted by [PERSON] applied machine learning techniques to predict in-hospital cardiac arrest from patients presenting to the ED, showing the potential of machine learning to support early clinical decision-making. However, that analysis focused on a specific critical outcome (cardiac arrest) and did not evaluate the performance of individual NEWS parameters across an unselected ED population using decision tree techniques [22]. Moreover, a retrospective study conducted by [PERSON] et al. compared the predictive performance of the NEWS and several other EWSs for 24 h ICU admission or death, finding the NEWS to have the highest accuracy [23]. However, this study did not analyze the contribution of individual NEWS parameters using decision tree methods across the entire ED cohort, which remains the primary focus of this work. Machine learning offers a promising approach to analyze and optimize the use of the NEWS in the ED [24; 25]. These algorithms can detect complex, non-linear interactions among variables, enabling more accurate identification of patients at risk of clinical deterioration in emergency settings [26].