# Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
## 1 Introduction  
The usage of artificial intelligence (AI) in various areas enhances the performance of proposed systems and helps end users of the systems. Personalized medicine, medication development, medical imaging analysis, and illness detection are all machine learning (ML) applications in healthcare [1, 2, 3]. Additionally, ML methods offer natural language processing for medical records, clinical decision support systems, epidemic prediction, wearable device monitoring, healthcare operations optimization, and predictive analytics for patient outcomes [1, 2, 3]. In emergency departments (EDs), early assessment, diagnosis, and referral of patients to appropriate areas (discharge from hospital, hospitalization, patient referral, etc.) are vital for ED functioning and the management of limited resources. It is an important issue for emergency departments to determine how the management of the patients will continue in the hospital, taking into account vital signs, medical tests, and demographic informationof the patients. The hypothesis of this study investigates whether predictive AI-based approaches can enhance the accuracy of forecasting ED disposition and assess the potential of such systems to support medical practitioners. This study aims to utilize the information gathered from patients in the emergency department to assist specialist doctors in making decisions regarding patient hospitalization through the application of decision-support approaches developed using machine learning models. With the increasing use of EDs in hospitals around the world, the uneven distribution of resources, delays in patient care, and subsequent increases in morbidity and mortality have become major public health issues [1, 2, 3]. EDs, where diagnostic and therapeutic interventions must be carried out quickly and effectively [4], are also where hospital outcomes are administered [5]. Evidence shows that the early prediction of hospital outcomes improves patient flow in the ED [6, 7]. Prediction models provide opportunities to identify high-risk patients and prioritize limited medical supplies. Today, artificial intelligence (AI) is used in many medical applications, especially to reinforce the decision-making process and improve healthcare services, and the ED is one of them [8]. Due to the rapid progress in the field of machine learning, it is inevitable that the impact of AI on working life in the ED will increase. Changes will take place in the mapping, measurement, and management phases. In the first stage, clinical problems will be identified, and AI methods will be explored. In this study, we worked on ML modeling in accordance with clinical problems in the emergency department [9]. Swift improvements in the applications of AI have revealed the possibility of utilizing the data from healthcare services to generate reliable machine learning (ML) models that can timely and dynamically automatize clinical decisions [10]. ED prediction models focus on a complex clinical judgment that depends on the patient's possible acute course, the status of medical resources, and local applications [11]. ML models have advantages such as reducing the load on EDs, preventing redundant testing, and contributing to a faster and more precise decision-making process for healthcare professionals [12]. Although the implementation of AI in the medical field is highly controversial in terms of ethical aspects, it is expected to be more widespread in the following years due to its contribution to the clinical decision-making process. The main objective of this study is to predict the outcomes (referral, discharge, hospitalization, and mortality) of ED patients with ML models and to compare the prediction performances of the models. ML-based models could help MDs in making accurate decisions regarding the outcomes of ED patients. This study aims to contribute to the literature by generating a substantial dataset, developing seven distinct models using seven different machine learning approaches, and incorporating 34 unique features for modeling.