Almulhim Mohammed, Alfaraj Dunya, Alabbad Dina, Alghamdi Faisal A, AlKhudair Mubarak A, AlKatout Khalid A, AlShehri Saud A, Alsulaibaikh Amal
Emergency Medicine Department, College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia.
Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia.
Acta Inform Med. 2025;33(2):152-157. doi: 10.5455/aim.2025.33.152-157.
Triage is a critical component of Emergency department care. Erroneous patient classification and mis-triaging are common in present triage systems worldwide. Therefore, several institutes worldwide have developed artificial intelligence-based algorithms that use machine learning approaches to sort and triage patients effectively.
This study aims were to propose a machine learning model to predict the triage level for emergency medicine department patients and compare its performance to the standard nursing triage system.
This retrospective pilot study collected the dataset of emergency department records from King Fahad Hospital of the University in khobar, between January 1, 2020, and December 31, 2022. A sample of 998 randomly selected patients was included in this cohort. The machine learning model was trained using 10-fold cross-validation. Two experiments were conducted, including five triage levels, and the second combing triage levels 2, 3, 4, and 5.
The machine learning model achieved an accuracy of 84% in experiment 1 and 64% in experiment 2. The mis-triage rates of the machine learning model were significantly lower than those of the standard nursing triage system.
The machine learning model achieved higher accuracy and lower mis-triage rates than the standard nursing triage system. Thus, the proposed machine learning model can be a helpful tool for emergency department triage, enabling more efficient and accurate patient management.
分诊是急诊科护理的关键组成部分。在全球目前的分诊系统中，错误的患者分类和误分诊很常见。因此，全球多家机构开发了基于人工智能的算法，使用机器学习方法对患者进行有效分类和分诊。
本研究旨在提出一种机器学习模型，以预测急诊科患者的分诊级别，并将其性能与标准护理分诊系统进行比较。
这项回顾性试点研究收集了2020年1月1日至2022年12月31日期间胡拜尔法赫德国王大学医院急诊科记录的数据集。该队列纳入了998名随机选择的患者样本。使用10折交叉验证对机器学习模型进行训练。进行了两个实验，第一个包括五个分诊级别，第二个将分诊级别2、3、4和5合并。
机器学习模型在实验1中的准确率为84%，在实验2中的准确率为64%。机器学习模型的误分诊率显著低于标准护理分诊系统。
机器学习模型比标准护理分诊系统具有更高的准确率和更低的误分诊率。因此，所提出的机器学习模型可以成为急诊科分诊的有用工具，实现更高效、准确的患者管理。
