Prioritizing Hospital Admission According to Emergency by using Machine Learning

Computer Engineering

This review categorizes the pertinent research into three main areas: hospital admission, discharge, and mortality prediction; ML-based triage systems; and disease prediction and detection. Intelligent systems and data-driven algorithm techniques being rapidly deployed in emergency medicine. The Advanced computational techniques, specifically machine learning algorithms, are utilized to the field of disease prediction, using datasets from medical photos and electronic health records (EHRs). Numerous methods have demonstrated efficacy in detecting patterns and predicting the course of diseases, including techniques such as logistic regression and naive Bayes classification, which enable random forests, multi-layer perceptrons (MLP), support vector classifiers (SVC), and long short-term memory networks (LSTM). Large amounts of structured and unstructured data can be analyzed and used to find subtle trends and statistical relationships, including correlations, which help uncover that human healthcare professionals might miss.

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