Brossard Cyrielle, Goetz Christophe, Catoire Pierre, Cipolat Lauriane, Guyeux Christophe, Gil Jardine Cédric, Akplogan Mahuna, Abensur Vuillaume Laure
Emergency department, CHR Metz-Thionville, Metz, 57000, France.
Université de Lorraine, Vandoeuvre les Nancy, France.
BMC Emerg Med. 2025 Jan 6;25(1):3. doi: 10.1186/s12873-024-01141-4.
Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.
The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.
We performed a retrospective multicenter study in two French ED from January 1st, 2010 to December 31st, 2019.We tested several machine learning algorithms and compared the results.
The arrival and departure times from the ED of 2 hospitals were collected from all consultations during the study period, then grouped into 87 600 one-hour slots. Through the development of two models (one for each location), we found that the XGBoost method with hyperparameter adaptations was the best, suggesting that the studied data could be predicted (mean absolute error) at 2.63 for Hospital 1 and 2.64 for Hospital 2).
This study ran the construction and validation of a powerful tool for predicting ED admissions in 2 French ED. This type of tool should be integrated into the overall organization of an ED, to optimize the resources of healthcare professionals.
急诊科过度拥挤是一个重大的公共卫生问题，导致团队工作量增加和疲惫不堪，进而产生不良后果。能够预测急诊科患者的入院情况似乎很有意思。
本研究的主要目的是使用人工智能构建并测试一种用于急诊科入院预测的工具。
我们于2010年1月1日至2019年12月31日在法国的两家急诊科进行了一项回顾性多中心研究。我们测试了几种机器学习算法并比较了结果。
在研究期间，从所有会诊中收集了两家医院急诊科的到达和离开时间，然后将其分组为87600个一小时时间段。通过开发两个模型（每个地点一个），我们发现经过超参数调整的XGBoost方法是最佳的，这表明研究数据可以被预测（医院1的平均绝对误差为2.63，医院2为2.64）。
本研究进行了一个用于预测法国两家急诊科入院情况的强大工具的构建和验证。这种类型的工具应整合到急诊科的整体组织中，以优化医护人员的资源。
