Prioritizing Hospital Admission According to Emergency Using Machine Learning

The use of artificial intelligence and machine learning in emergency medicine is expanding swiftly. This paper evaluates studies in the domain, organizing them into three groups: disease prediction and detection, hospital admission, discharge, and mortality prediction, and machine learning-driven triage systems. The first group includes studies employing machine learning algorithms with techniques like Logistic Regression, Naive Bayes, Random Forest, MLP, SVC, LSTM, and data from electronic health records and medical imaging. The second group aims to predict hospital admission, discharge, and mortality through machine learning, utilizing datasets such as electronic health records, lab data, and vital signs, with decision trees, random forests, and logistic regression as common algorithms. The third group discusses creating machine learning-based triage systems in emergency departments, using datasets like vital signs and medical history, and techniques including decision trees, neural networks, and fuzzy logic. The reviewed studies highlight the promise of AI and machine learning in emergency medicine, noting that algorithmic accuracy and efficacy hinge on data quality and volume. Continued research is essential to corroborate these results and enhance algorithm performance in real-world settings.