# Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
## 1 Introduction
### Related Work  
Previous studies have set the groundwork for the application of machine learning in healthcare. [PERSON] et al. [9] utilized associative classification and odds ratios to predict in-hospital patient mortality using the MIMIC-IV dataset. [PERSON] et al. [10] developed a predictive model to assess mortality risks in patients with sepsis-related acute respiratory failure. Building upon these foundational works, our approach seeks to extend these methodologies by integrating more granular data handling and advanced machine-learning techniques. Additionally, [PERSON] et al. [11] established a benchmark database using the MIMIC-IV ED database. Our work enhances this model by employing a comprehensive AutoML platform that simplifies model selection and tuning, potentially improving accuracy and usability for end-users in clinical settings. The field of medical triage has seen varied approaches to predictive modeling. [PERSON] and [PERSON] [12] reported on a machine-learning pre-diagnosis model for EDs, achieving a minimum accuracy of 75.5%, while [PERSON] et al. [13] utilized demographic and temporal variables in a logistic regression model to predict hospital admissions effectively. Our study advances these models by implementing a Gradient Boosting Machine (GBM) model that not only considers a broader range of variables but also integrates interactions between them, providing a more nuanced understanding of patient outcomes. Moreover, [PERSON] et al. [14] explored multiple algorithms, including decision trees and neural networks, to enhance predictive capabilities. Contrasting with these, our model uniquely applies AutoML to streamline the selection and optimization process, identifying the most effective algorithms based on performance metrics. This approach differs markedly from the more manual, algorithm-specific enhancements used by [PERSON] et al. [15] and [PERSON] et al. [16], who employed linear programming and conventional statistical methods, respectively. Recent studies have demonstrated the efficacy of machine-learning techniques in predicting hospital admissions from emergency department data. [PERSON] et al. explored the application of machine-learning algorithms to forecast hospital admissions, highlighting their potential to improve decision-making in emergency care settings [17]. In subsequent studies, the authors focused on different aspects of this approach. They used machine learning for predicting the hospitalization of emergency department patients, providing insights into how these models can be tailored to specific clinical needs [18]. Another study by the same group employed exploratory clustering techniques to better understand patient data patterns and improve prediction accuracy [19]. Additionally, they investigated the prediction of hospitalization using a broader range of variables, emphasizing the model's robustness and adaptability in various emergency department scenarios [20]. These efforts underscore the growing importance of integrating machine learning into clinical workflows to support healthcare professionals in making more informed and timely decisions. Our research contributes significantly to the field by advancing the integration of machine learning into the triage process. We aim not only to predict outcomes but also to understand the underlying factors that contribute to these outcomes, which is crucial in scenarios involving readmissions and early bounce-backs previously explored through deep-learning models [21; 22]. By offering a model that is both transparent and adaptable, our approach sets a new standard for predictive accuracy and ethical AI usage in medical triage. Our study introduces an innovative approach to emergency department (ED) triage using automated machine-learning (AutoML) technologies applied to the MIMIC-IV-ED database. This research is pivotal as it enhances decision-making processes at a critical first point of contact in healthcare systems--emergency triage.