用MIMIC-IV数据验证了本地急诊入院预测模型的可靠性。
Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV
- 用MIMIC-IV数据验证五种算法，随机森林表现最优。
- 随机森林在测试集上达到AUC-ROC 0.9999，灵敏度0.9997。
- 为小规模本地模型提供可复现的验证基准，适合医疗决策研究者。
有效管理急诊科（ED）拥挤对改善患者预后和优化医疗资源配置至关重要。本研究基于希腊某医院的小规模本地数据集构建的入院预测模型，利用全面的MIMIC-IV数据集进行验证。数据预处理后，评估了五种算法：线性判别分析（LDA）、K近邻（KNN）、随机森林（RF）、递归分割与回归树（RPART）和支持向量机（SVM Radial）。其中，随机森林表现最佳，在MIMIC-IV数据上实现受试者工作特征曲线下面积（AUC-ROC）0.9999，敏感度0.9997，特异性0.9999。结果表明，随机森林在处理复杂数据集进行入院预测方面具有强鲁棒性，确立了MIMIC-IV作为小规模本地模型验证的重要基准，并为改进急诊管理策略提供了可操作的洞见。
原文摘要 · Abstract (English)
The effective management of Emergency Department (ED) overcrowding is essential for improving patient outcomes and optimizing healthcare resource allocation. This study validates hospital admission prediction models initially developed using a small local dataset from a Greek hospital by leveraging the comprehensive MIMIC-IV dataset. After preprocessing the MIMIC-IV data, five algorithms were evaluated: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM Radial). Among these, RF demonstrated superior performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.9999, sensitivity of 0.9997, and specificity of 0.9999 when applied to the MIMIC-IV data. These findings highlight the robustness of RF in handling complex datasets for admission prediction, establish MIMIC-IV as a valuable benchmark for validating models based on smaller local datasets, and provide actionable insights for improving ED management strategies.
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