### Related Work  
Using the MIMIC-IV dataset, [PERSON] et al. predicted in-hospital mortality in lung cancer patients admitted to intensive care unit [18]. [PERSON] et al. created a predictive model for evaluating the mortality risk of patients with sepsis-related acute respiratory failure [19]. Similarly, [PERSON] et al. created a benchmark database based on the MIMIC-IV emergency department database, combining data from multiple tables using four unique identifiers for data linkage [20]. In addition to leveraging the MIMIC-IV dataset, our literature review reveals a spectrum of machine-learning methodologies applied to hospital admission predictions. [PERSON] et al. utilized a machine learning model in EDs with commendable accuracy; however, this model requires extensive data preprocessing, which can be a limiting factor in time-sensitive environments [21]. Decision trees and support vector machines, as used by [PERSON] et al., provide intuitive decision-making pathways and robust classification capabilities, but they often suffer from overfitting and scalability issues, particularly when dealing with large datasets like those from EDs [22]. The extreme gradient boosting method presents an efficient handling of varied data types and missing values, a clear advantage in clinical settings. Nonetheless, it also brings a computational complexity that necessitates careful tuning to avoid overfitting [22]. Logistic regression and artificial neural networks, while widely used for their simplicity and flexibility, respectively, may not capture complex nonlinear relationships as effectively as ensemble methods [22]. The work by [PERSON] et al., which focused on predicting children's admissions from triage data, showed the potential for ML to complement conventional triage approaches. [23]. However, it is important to note that such models can be highly specific to the datasets they were trained on, limiting their generalizability [23]. The Random Forest classifier, found to perform optimally by [PERSON] et al., offers a robust alternative with its ensemble approach, reducing the risk of overfitting. However, it can be less interpretable, which is a crucial consideration in clinical settings where understanding the model's reasoning is vital [24]. It is worth noting that advancements in the same domain have been made by another study [25], which used a clustering-related technique, the k-means algorithm, to categorize ED patients and compare its efficacy with the traditional admission methods. Their efforts aimed to reduce ED overcrowding by optimizing the triage process, thus enhancing the quality of healthcare services. The k-means clustering algorithm offers several advantages but also carries notable limitations. Among its benefits, k-means can process large datasets efficiently and provide a systematic, objective categorization of patients, which is crucial for the fast-paced environment of the ED. This method aids in optimizing resource allocation, thereby potentially reducing wait times and improving patient flow. Moreover, it scales well to accommodate growing data volumes and supports data-driven decision making. However, the algorithm also has drawbacks, such as sensitivity to outliers, which can lead to misclassification. It requires the number of clusters (k) to be predetermined, which can be challenging without prior insight into the optimal categories. Additionally, k-means assumes clusters are of similar size and spherical shapes, which might not apply well to the complexity of medical data. The initial placement of centroids can affect the final outcome, introducing potential instability across different runs. Furthermore, the simplification of complex medical conditions into neat clusters might risk inadequate triage decisions if not carefully integrated with expert clinical judgments. These factors highlight the need for a balanced approach, combining algorithmic efficiency with clinical expertise to enhance the triage process in emergency settings.