Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review
Dublin Core
Title
Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review
Subject
In Emergency Departments (EDs), triage is crucial for determining patient severity and prioritizing care, typically using the Manchester Triage Scale (MTS). Traditional triage systems, reliant on human judgment, are prone to under-triage and over-triage, resulting in variability, bias, and incorrect patient classification. Studies suggest that Machine Learning (ML) and Natural Language Processing (NLP) could enhance triage accuracy and consistency. This review analyzes studies on ML and/or NLP algorithms for ED patient triage.
Description
Sixty studies covering 57 ML algorithms were included. Logistic Regression (LR) was the most used model, while eXtreme Gradient Boosting (XGBoost), decision tree-based algorithms with Gradient Boosting (GB), and Deep Neural Networks (DNNs) showed superior performance. Frequent predictive variables included demographics and vital signs, with oxygen saturation, chief complaints, systolic blood pressure, age, and mode of arrival being the most retained. The ML algorithms showed significant bias risk due to critical bias assessment in classification models.
Creator
Bruno Matos Porto
Source
https://bmcemergmed.biomedcentral.com/articles/10.1186/s12873-024-01135-2
Publisher
BMC Emergency Medicine
Date
18 november 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
text
Files
Collection
Citation
Bruno Matos Porto, “Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review,” Repository Horizon University Indonesia, accessed July 6, 2025, https://repository.horizon.ac.id/items/show/9412.