Prioritizing Hospital Admission According to Emergency using Machine Learning

The use of artificial intelligence and machine learning techniques in emergency medicine has grown rapidly. This paper reviews and assesses studies in this field, categorizing them into three areas: prediction and detection of disease, prediction of need for admission, discharge, and mortality, and machine learning-based triage systems. In the first category, several studies have been conducted using machine learning algorithms. These studies used various machine learning techniques, including Logistic Regression, Naive Bias, Random Forest, MLP, SVC, LSTM, and datasets from electronic health records and medical imaging. The second category focuses on predicting the need for hospital admission, discharge, and mortality using machine learning algorithms. These studies have used various datasets, including electronic health records, laboratory data, and vital signs. The algorithms used in these studies include decision trees, random forests, and logistic regression models. Finally, the third category explores the development of machine learning-based triage systems for emergency departments. These studies have used various datasets, including vital signs and medical history, and machine learning techniques such as decision trees, artificial neural networks, and fuzzy logic. Overall, the studies reviewed in this paper demonstrate the potential of artificial intelligence and machine learning techniques in emergency medicine. 

KEYWORDS: Logistic Regression, Naive Bias, Random Forest, MLP, SVC, LSTM