利用新型罗马尼亚电子健康记录数据集进行败血症预后预测的可解释机器学习
Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset
AI总结：
研究通过可解释机器学习模型预测败血症预后，利用新型电子健康记录数据集，识别强临床预测因子并实现三种分类任务的先进性能。
摘要：
我们开发并分析了用于败血症预后预测的可解释机器学习（ML）模型，使用来自罗马尼亚大型急诊医院的新型电子健康记录（EHR）数据集，包含12,286次住院记录的 demographics、国际疾病分类（ICD-10）诊断和600种实验室测试类型。本研究旨在识别临床强预测因子，同时在三种分类任务中实现最先进的性能：（1）死亡 vs 放弃，（2）死亡 vs 恢复，（3）恢复 vs 改善。我们训练了五种ML模型以捕捉复杂分布并保持临床可解释性。实验探索了特征丰富性和患者覆盖之间的权衡，使用10-50种最频繁的实验室测试子集。模型性能通过准确率和曲线下面积（AUC）评估，可解释性通过SHapley Additive exPlanations（SHAP）评估。最高性能出现在死亡 vs 恢复案例研究中（AUC=0.983，准确率=0.93）。SHAP分析识别出几个强预测因子，如心血管共病、尿素水平、天门冬氨酸转氨酶、血小板计数和嗜酸性粒细胞百分比。嗜酸性粒细胞减少症成为顶级预测因子，突显了其作为未被当前评估标准纳入的有价值标记物的重要性，同时高性能表明这些模型在临床环境中的适用性。
英文摘要：
We develop and analyze explainable machine learning (ML) models for sepsis outcome prediction using a novel Electronic Health Record (EHR) dataset from 12,286 hospitalizations at a large emergency hospital in Romania. The dataset includes demographics, International Classification of Diseases (ICD-10) diagnostics, and 600 types of laboratory tests. This study aims to identify clinically strong predictors while achieving state-of-the-art results across three classification tasks: (1)deceased vs. discharged, (2)deceased vs. recovered, and (3)recovered vs. ameliorated. We trained five ML models to capture complex distributions while preserving clinical interpretability. Experiments explored the trade-off between feature richness and patient coverage, using subsets of the 10--50 most frequent laboratory tests. Model performance was evaluated using accuracy and area under the curve (AUC), and explainability was assessed using SHapley Additive exPlanations (SHAP). The highest performance was obtained for the deceased vs. recovered case study (AUC=0.983, accuracy=0.93). SHAP analysis identified several strong predictors such as cardiovascular comorbidities, urea levels, aspartate aminotransferase, platelet count, and eosinophil percentage. Eosinopenia emerged as a top predictor, highlighting its value as an underutilized marker that is not included in current assessment standards, while the high performance suggests the applicability of these models in clinical settings.
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