Sitthiprawiat Patipan, Wittayachamnankul Borwon, Sirikul Wachiranun, Laohavisudhi Korsin
Department of Emergency Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Department of Community Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Sci Rep. 2025 Aug 25;15(1):31212. doi: 10.1038/s41598-025-17180-1.
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.
急诊科过度拥挤会导致患者护理延迟和临床结果恶化。传统的分诊系统存在准确性和一致性方面的局限性。本研究开发并内部验证了一种机器学习模型，用于预测急诊科患者的重症监护病房（ICU）收治情况和资源利用情况。对清迈玛哈那空医院163452次急诊科就诊（2018 - 2022年）进行回顾性分析，将逻辑回归、随机森林和XGBoost模型与加拿大分诊及 acuity 量表（CTAS）进行比较。XGBoost模型表现出卓越的预测性能（曲线下面积[AUROC]为0.917，而其他模型为0.882；精确率-召回率曲线下面积[AUPRC]为0.629，而其他模型为0.333）。关键预测因素包括到达方式、患者年龄以及通过多语言句子嵌入分析的自由文本主诉。这些结果表明，与传统分诊方法相比，纳入非结构化文本数据的机器学习有潜力通过更有效地识别重症患者来提高分诊准确性和资源分配。
