Chen Junde, Qi Trudi Di, Vu Jacqueline, Wen Yuxin
Fowler School of Engineering, Chapman University, Orange 92866, CA, USA.
Fowler School of Engineering, Chapman University, Orange 92866, CA, USA.
J Biomed Inform. 2023 Nov;147:104526. doi: 10.1016/j.jbi.2023.104526. Epub 2023 Oct 17.
Accurate prediction of the Length of Stay (LoS) and mortality in the Intensive Care Unit (ICU) is crucial for effective hospital management, and it can assist clinicians for real-time demand capacity (RTDC) administration, thereby improving healthcare quality and service levels.
This paper proposes a novel one-dimensional (1D) multi-scale convolutional neural network architecture, namely 1D-MSNet, to predict inpatients' LoS and mortality in ICU. First, a 1D multi-scale convolution framework is proposed to enlarge the convolutional receptive fields and enhance the richness of the convolutional features. Following the convolutional layers, an atrous causal spatial pyramid pooling (SPP) module is incorporated into the networks to extract high-level features. The optimized Focal Loss (FL) function is combined with the synthetic minority over-sampling technique (SMOTE) to mitigate the imbalanced-class issue.
On the MIMIC-IV v1.0 benchmark dataset, the proposed approach achieves the optimum R-Square and RMSE values of 0.57 and 3.61 for the LoS prediction, and the highest test accuracy of 97.73% for the mortality prediction.
The proposed approach presents a superior performance in comparison with other state-of-the-art, and it can effectively perform the LoS and mortality prediction tasks.
准确预测重症监护病房（ICU）的住院时长（LoS）和死亡率对于有效的医院管理至关重要，它可以帮助临床医生进行实时需求容量（RTDC）管理，从而提高医疗质量和服务水平。
本文提出了一种新颖的一维（1D）多尺度卷积神经网络架构，即1D-MSNet，用于预测ICU住院患者的LoS和死亡率。首先，提出了一个1D多尺度卷积框架，以扩大卷积感受野并增强卷积特征的丰富性。在卷积层之后，将空洞因果空间金字塔池化（SPP）模块纳入网络以提取高级特征。优化后的焦点损失（FL）函数与合成少数过采样技术（SMOTE）相结合，以缓解类不平衡问题。
在MIMIC-IV v1.0基准数据集上，所提出的方法在LoS预测方面实现了0.57和3.61的最佳R平方和均方根误差（RMSE）值，在死亡率预测方面达到了97.73%的最高测试准确率。
与其他现有技术相比，所提出的方法表现出卓越的性能，并且可以有效地执行LoS和死亡率预测任务。
