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.