| Summary: | Abstract Triage procedure is used in Emergency Departments (ED) to manage the patient's treatment and prioritise care access. This is a largely resource‐consuming phase and relevant to reduce risk and optimise resource management. Moreover, the presence of patients in the ED (both in treatment rooms and in waiting rooms after triage) may increase the patients' time of stay, thus creating problems for critical patients and for healthcare process management. Moreover, it has been proved that a large fraction of ED incoming patients do not require emergency treatments and might be treated in ambulatory or by family doctors. In such cases, the triage wastes resources and time. In addition, the decision of a low priority or no ED necessity is relevant considering that underestimating treatment necessity may cause errors in patient treatments. Improving triage related decisions is a relevant task. It has been shown that computational methods such as machine learning (ML) may support triage by providing better stratification of patients as well as better results in terms of outcome. We here present a literature review discussing some recent approaches to predict the severity of patients and in particular, we present recent approaches based on ML. We use PRISMA methodology to include works in our analysis. Finally, the future directions of research and open problems are highlighted.
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