arXiv 2407.110962024-07-15cs.LGcs.AI

静态与多变量时序注意力融合Transformer用于再入院风险预测

Static and multivariate-temporal attentive fusion transformer for readmission risk prediction

School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）；Cooperative Innovation Center of Internet Healthcare, Zhengzhou University（郑州大学互联网医疗协同创新中心）；Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家心血管病中心阜外医院）

Zhe Sun, Runzhi Li, Jing Wang, Gang Chen, Siyu Yan, Lihong Ma

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AI总结：

针对ICU患者短期再入院预测，提出静态与多变量时序注意力融合Transformer（SMTAFormer），通过MLP与时序Transformer分别提取特征并融合，在MIMIC-III数据集上准确率达86.6%，AUC达0.717。

摘要：

背景：对ICU患者进行准确的短期再入院预测，对于通过协助医生做出出院决策来提高资源分配效率具有重要意义。临床上，从ICU监护仪收集的个体静态数据和多变量时序数据在短期再入院预测中均发挥关键作用。信息丰富的静态和多变量时序特征表示的有效捕获与融合，对准确再入院预测构成挑战。方法：我们提出了一种新颖的静态与多变量时序注意力融合Transformer（SMTAFormer），通过充分利用人口统计学和动态时序数据的潜力来预测ICU患者的短期再入院。在SMTAFormer中，我们首先应用MLP网络和时序Transformer网络分别学习有用的静态和时序特征表示。然后，通过精心设计的静态与多变量时序特征融合模块，对多变量时序特征间的内部相关性进行建模，并构建静态特征与多变量时序特征间的相互相关性，从而融合静态和时序特征表示。结果：我们基于MIMIC-III数据集构建了再入院风险评估（RRA）数据集。大量实验表明，SMTAFormer优于先进方法，其中我们提出方法的准确率高达86.6%，受试者工作特征曲线下面积（AUC）高达0.717。结论：我们提出的SMTAFormer能够高效捕获并融合静态和多变量时序特征表示。结果表明，通过与强基线的比较，SMTAFormer显著提升了ICU患者短期再入院预测性能。

英文摘要：

Background: Accurate short-term readmission prediction of ICU patients is significant in improving the efficiency of resource assignment by assisting physicians in making discharge decisions. Clinically, both individual static static and multivariate temporal data collected from ICU monitors play critical roles in short-term readmission prediction. Informative static and multivariate temporal feature representation capturing and fusion present challenges for accurate readmission prediction. Methods:We propose a novel static and multivariate-temporal attentive fusion transformer (SMTAFormer) to predict short-term readmission of ICU patients by fully leveraging the potential of demographic and dynamic temporal data. In SMTAFormer, we first apply an MLP network and a temporal transformer network to learn useful static and temporal feature representations, respectively. Then, the well-designed static and multivariate temporal feature fusion module is applied to fuse static and temporal feature representations by modeling intra-correlation among multivariate temporal features and constructing inter-correlation between static and multivariate temporal features. Results: We construct a readmission risk assessment (RRA) dataset based on the MIMIC-III dataset. The extensive experiments show that SMTAFormer outperforms advanced methods, in which the accuracy of our proposed method is up to 86.6%, and the area under the receiver operating characteristic curve (AUC) is up to 0.717. Conclusion: Our proposed SMTAFormer can efficiently capture and fuse static and multivariate temporal feature representations. The results show that SMTAFormer significantly improves the short-term readmission prediction performance of ICU patients through comparisons to strong baselines.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。