Lab of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Greece.
AHEPA General University Hospital of Thessaloniki, Greece.
Stud Health Technol Inform. 2024 Aug 22;316:914-918. doi: 10.3233/SHTI240560.
The overwhelming volume of patients in emergency departments (EDs) is a significant problem that hinders the delivery of high quality healthcare. Despite their great value, triage protocols are challenging to put into practice. This paper examines the utility of prediction models as a tool for clinical decision support, with a focus on medium-severity patients as defined by the ESI algorithm. 689 cases of medium-risk patients were gathered from the AHEPA hospital, evaluated, and their data fed three classifiers: XGBoost (XGB), Random Forest (RF) and Logistic Regression (LR), with the prediction goal being the outcome of their visit, i.e. admission or discharge. Essential features for the prediction task were determined using feature importance and distribution analysis. Despite having many missing values or high sparsity datasets, several symptoms and metrics are recommended as crucial for outcome prediction. When fed the patients' vital signs, XGB achieved an accuracy score of 91.30%. Several chief complaints were also proven beneficial. Prediction models can, in general, not only lessen the drawbacks of triage implementation, but also enhance its delivery.
急诊科（ED）患者人数过多是一个严重的问题，它阻碍了高质量医疗保健的提供。尽管分诊协议具有很大的价值，但实施起来具有挑战性。本文研究了预测模型作为临床决策支持工具的效用，重点关注 ESI 算法定义的中度严重程度的患者。从 AHEPA 医院收集了 689 例中度风险患者进行评估，并将其数据输入三个分类器：XGBoost（XGB）、随机森林（RF）和逻辑回归（LR），预测目标是他们就诊的结果，即入院或出院。使用特征重要性和分布分析确定了预测任务的基本特征。尽管存在许多缺失值或高稀疏数据集，但仍推荐了一些症状和指标作为预测结果的关键因素。当输入患者的生命体征时，XGB 的准确率达到 91.30%。一些主要的抱怨也被证明是有益的。预测模型不仅可以减轻分诊实施的缺点，还可以提高分诊的效果。
