用首24小时病历数据预测重症患者多类病情恶化，提升预警精准度。
Early Prediction of Multi-Label Care Escalation Triggers in the Intensive Care Unit Using Electronic Health Records
- 基于首24小时生命体征和检验数据，构建多标签分类模型预测四类危重信号。
- XGBoost模型在呼吸、循环、肾功能和神经功能恶化上分别达0.66、0.72、0.76、0.62的F1分数。
- 结果可解释性强，无需复杂时序建模，适合临床实时预警系统部署。
重症监护室（ICU）患者常呈现多重生理恶化的复杂表现，需及时升级治疗。传统预警系统如SOFA或MEWS侧重单一结局，难以捕捉临床衰退的多维特征。本研究提出一种多标签分类框架，利用患者入院后前24小时的电子病历数据，预测包括呼吸衰竭、血流动力学不稳、肾功能受损及神经功能恶化在内的护理升级触发事件（CETs）。采用MIMIC-IV数据库，通过规则定义小时24至72之间的CET标准（如血氧饱和度低于90%、平均动脉压低于65 mmHg、肌酐升高超过0.3 mg/dL，或格拉斯哥昏迷评分下降超过2分）。特征提取涵盖生命体征聚合值、实验室指标及静态人口统计信息。在85,242例ICU住院病例中进行训练（80%，68,193例）与测试（20%，17,049例）。评估指标包括各标签的精确率、召回率、F1分数及汉明损失。最优模型XGBoost在呼吸、循环、肾功能和神经功能恶化上的F1分数分别为0.66、0.72、0.76和0.62，优于基线模型。特征分析显示呼吸频率、血压、肌酐等临床参数为关键预测因子，与各触发事件的临床定义一致。该框架展现出无需复杂时间序列建模或自然语言处理即可实现早期、可解释性临床警报的实用潜力。
原文摘要 · Abstract (English)
Intensive Care Unit (ICU) patients often present with complex, overlapping signs of physiological deterioration that require timely escalation of care. Traditional early warning systems, such as SOFA or MEWS, are limited by their focus on single outcomes and fail to capture the multi-dimensional nature of clinical decline. This study proposes a multi-label classification framework to predict Care Escalation Triggers (CETs), including respiratory failure, hemodynamic instability, renal compromise, and neurological deterioration, using the first 24 hours of ICU data. Using the MIMIC-IV database, CETs are defined through rule-based criteria applied to data from hours 24 to 72 (for example, oxygen saturation below 90, mean arterial pressure below 65 mmHg, creatinine increase greater than 0.3 mg/dL, or a drop in Glasgow Coma Scale score greater than 2). Features are extracted from the first 24 hours and include vital sign aggregates, laboratory values, and static demographics. We train and evaluate multiple classification models on a cohort of 85,242 ICU stays (80 percent training: 68,193; 20 percent testing: 17,049). Evaluation metrics include per-label precision, recall, F1-score, and Hamming loss. XGBoost, the best performing model, achieves F1-scores of 0.66 for respiratory, 0.72 for hemodynamic, 0.76 for renal, and 0.62 for neurologic deterioration, outperforming baseline models. Feature analysis shows that clinically relevant parameters such as respiratory rate, blood pressure, and creatinine are the most influential predictors, consistent with the clinical definitions of the CETs. The proposed framework demonstrates practical potential for early, interpretable clinical alerts without requiring complex time-series modeling or natural language processing.
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