基于多模态融合建模的ICU脓毒症预后研究

Research on ICU Sepsis Prognosis Based on Multimodal Fusion Modeling

摘要: 脓毒症是ICU中的常见危急病症，其早期识别和预后预测对于降低病死率至关重要。本研究旨在通过多模态数据融合技术构建脓毒症预后预测模型，结合静态基线数据、时间序列实验室数据以及电子病历文本数据，提升预测的准确性和稳健性。模型训练中采用了交叉验证和网格搜索相结合的超参数优化方法，并引入了L2正则化和Dropout技术以防止模型过拟合。此外，针对数据类别不平衡问题，使用Focal Loss损失函数提升了模型对少数类样本的识别能力。结果表明，基于多模态融合的模型在准确率、特异性、F1分数以及AUROC等关键指标上均表现优异，特别是在处理复杂的医疗数据时展现出较高的鲁棒性和泛化能力。本文的研究验证了多模态数据融合框架在脓毒症预后预测中的有效性，并为临床应用提供了有力支持。

Abstract: Sepsis is a common and critical condition in ICUs, and early identification and prognosis prediction are essential for reducing mortality rates. This study aims to develop a sepsis prognosis prediction model using multimodal data fusion techniques, integrating static baseline data, time-series laboratory data, and electronic health record (EHR) text data to improve prediction accuracy and robustness. The model training process employed a combination of cross-validation and grid search for hyperparameter optimization, and L2 regularization along with Dropout techniques was introduced to prevent overfitting. Additionally, to address the class imbalance issue in the dataset, the Focal Loss function was applied to enhance the model’s ability to identify minority class samples. The results demonstrate that the multimodal fusion-based model outperformed in key metrics such as accuracy, specificity, F1 score, and AUROC, particularly exhibiting high robustness and generalization capabilities when handling complex medical data. This study validates the effectiveness of the multimodal data fusion framework in sepsis prognosis prediction and provides strong support for potential clinical applications.