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<p>Emergency Departments (EDs) play an important role in providing critical healthcare services, especially in the context of acute conditions. The Manchester Triage System (MTS) was developed to assess the severity of patients arriving at EDs, ensuring that patients with more severe conditions receive immediate treatment. However, the presence of biases, resulting from factors such as a lack of adequate training and the influence of personal preferences of healthcare professionals, may compromise the waiting time for care. This study aims to improve the precision and efficiency of patient risk assessment within EDs, focusing on the MTS utilization. As a novel contribution, this work evaluates ensemble learning within the MTS framework and investigates the feasibility of risk classification using objective numerical features. Several Machine Learning (ML) models were explored using relevant patient information from a public MTS dataset, including Decision Tree, K-Nearest Neighbor, Random Forest, Multilayer Perceptron, and ensemble methods such as Soft and Hard Voting, and Stacking. Two experiments were conducted: the first combined categorical and numerical features; the second focused on numerical variables, except for the hospital identifier, included to control for inter-institutional variation. The results for the risk classification task were promising in terms of accuracy (ACC) and F1-Score, with up to 99.00% and 0.99, respectively, for Experiment&#xa0;#1, which achieved the highest predictive performance. For Experiment&#xa0;#2, an ACC of up to 74.68% and an F1-Score of 0.74 were achieved for the Stacking ensemble ML method, indicating clinically meaningful performance. These results suggest that the proposed approach may support risk classification in the MTS. However, further real-world evaluation is required due to the retrospective design and lack of prospective validation.</p>

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Ensemble learning approach to support Manchester protocol triage

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Leticia Silva,
  • Alan Floriano,
  • Carlos Valadão,
  • Lucas Lampier,
  • Eliete Caldeira,
  • Teodiano Bastos-Filho

摘要

Emergency Departments (EDs) play an important role in providing critical healthcare services, especially in the context of acute conditions. The Manchester Triage System (MTS) was developed to assess the severity of patients arriving at EDs, ensuring that patients with more severe conditions receive immediate treatment. However, the presence of biases, resulting from factors such as a lack of adequate training and the influence of personal preferences of healthcare professionals, may compromise the waiting time for care. This study aims to improve the precision and efficiency of patient risk assessment within EDs, focusing on the MTS utilization. As a novel contribution, this work evaluates ensemble learning within the MTS framework and investigates the feasibility of risk classification using objective numerical features. Several Machine Learning (ML) models were explored using relevant patient information from a public MTS dataset, including Decision Tree, K-Nearest Neighbor, Random Forest, Multilayer Perceptron, and ensemble methods such as Soft and Hard Voting, and Stacking. Two experiments were conducted: the first combined categorical and numerical features; the second focused on numerical variables, except for the hospital identifier, included to control for inter-institutional variation. The results for the risk classification task were promising in terms of accuracy (ACC) and F1-Score, with up to 99.00% and 0.99, respectively, for Experiment #1, which achieved the highest predictive performance. For Experiment #2, an ACC of up to 74.68% and an F1-Score of 0.74 were achieved for the Stacking ensemble ML method, indicating clinically meaningful performance. These results suggest that the proposed approach may support risk classification in the MTS. However, further real-world evaluation is required due to the retrospective design and lack of prospective validation.