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<p>Emergency department (ED) overcrowding contributes to delayed patient care and worse clinical outcomes. Traditional triage systems face accuracy and consistency limitations. This study developed and internally validated a machine learning model predicting intensive care unit (ICU) admissions and resource utilization in ED patients. A retrospective analysis of 163,452 ED visits (2018–2022) from Maharaj Nakhon Chiang Mai Hospital evaluated logistic regression, random forest, and XGBoost models against the Canadian Triage and Acuity Scale (CTAS). The XGBoost model achieved superior predictive performance (AUROC 0.917 vs. 0.882, AUPRC 0.629 vs. 0.333). Key predictors included mode of arrival, patient age, and free-text chief complaints analyzed with multilingual sentence embeddings. These results demonstrate that machine learning, incorporating unstructured text data, has the potential to enhance triage accuracy and resource allocation by more effectively identifying critically ill patients compared to traditional triage methods.</p>

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Development and internal validation of an AI-based emergency triage model for predicting critical outcomes in emergency department

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月25日

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  • Patipan Sitthiprawiat,
  • Borwon Wittayachamnankul,
  • Wachiranun Sirikul,
  • Korsin Laohavisudhi

摘要

Emergency department (ED) overcrowding contributes to delayed patient care and worse clinical outcomes. Traditional triage systems face accuracy and consistency limitations. This study developed and internally validated a machine learning model predicting intensive care unit (ICU) admissions and resource utilization in ED patients. A retrospective analysis of 163,452 ED visits (2018–2022) from Maharaj Nakhon Chiang Mai Hospital evaluated logistic regression, random forest, and XGBoost models against the Canadian Triage and Acuity Scale (CTAS). The XGBoost model achieved superior predictive performance (AUROC 0.917 vs. 0.882, AUPRC 0.629 vs. 0.333). Key predictors included mode of arrival, patient age, and free-text chief complaints analyzed with multilingual sentence embeddings. These results demonstrate that machine learning, incorporating unstructured text data, has the potential to enhance triage accuracy and resource allocation by more effectively identifying critically ill patients compared to traditional triage methods.