基于MIMIC-IV数据使用时间卷积网络预测ICU患者未来器官功能障碍

中文摘要 AI 辅助

预测重症监护病房（ICU）患者未来的器官功能障碍对于早期临床干预至关重要，但现有的机器学习方法大多将序贯器官衰竭评估（SOFA）评分作为二元死亡率预测的输入，而非将其本身作为连续的临床结局。本研究探究时间卷积网络（TCN）从MIMIC-IV提取的多变量ICU时间序列数据中预测次日SOFA评分的能力，明确各器官系统对总SOFA方差及恶化的相对贡献，并识别ICU住院期间的不同轨迹模式。采用三日滑动窗口训练的残差TCN，在五折交叉验证中取得R²为0.740±0.013、平均绝对误差（MAE）为1.431±0.022，在均方根误差（RMSE）和R²指标上优于朴素的持续性基线模型。SHAP可解释性分析显示，该模型主要作为严重程度锚定机制而非真正的序列模型，预测几乎完全由最近的观察日主导。心血管功能障碍成为横断面严重程度和急性恶化的最强判别因素，无监督轨迹聚类识别出两类具有临床意义的表型：改善组（58.9%）和持续重症组（41.1%），二者在心血管、肝脏、凝血及肾脏受累情况上存在差异。研究结论指出，TCN可从ICU生理数据中提取有意义的预测信号，但目前较短的输入窗口和完整病例选择偏差限制了其临床实用性，为未来关于更长输入范围、替代缺失数据策略及外部验证的研究提供了方向。

英文摘要

Predicting future organ dysfunction in Intensive Care Unit (ICU) patients is critical for early clinical intervention, yet existing machine learning approaches have largely treated the Sequential Organ Failure Assessment (SOFA) score as an input to binary mortality prediction rather than as a continuous clinical outcome in its own right. We investigate the extent to which a Temporal Convolutional Net work (TCN) can predict next-day SOFA scores from multivariate ICU time-series data extracted from MIMIC-IV, characterise the relative contribution of each organ system to total SOFA variance and deterioration, and identify distinct trajectory patterns across ICU stays. A residual TCN trained on three-day sliding windows achieved a five-fold cross-validation R2 of 0.740 +- 0.013 and MAE of 1.431 +- 0.022, outperforming a naive persistence baseline on RMSE and R2. SHAP interpretability analysis revealed that the model functions primarily as a severity-anchoring mechanism rather than a true sequence model, with predictions dominated almost entirely by the most recent observation day. Cardiovascular dysfunction emerged as the strongest discriminator of both cross-sectional severity and acute deterioration, and unsupervised trajectory clustering identified two clinically meaningful phenotypes, an improving group (58.9%) and a persistently severe group (41.1%), differentiated by cardiovascular, hepatic, coagulation, and renal involvement. We conclude that TCNs can extract meaningful predictive signal from ICU physiological data, but that short input windows and complete-case selection bias currently limit their clinical utility, motivating future work on longer input horizons, alternative missing-data strategies, and external validation.

发表机构

• University of Sheffield（谢菲尔德大学）

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