A deep learning approach for inpatient length of stay and mortality prediction
A deep learning approach for inpatient length of stay and mortality prediction
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DOI：
10.1016/j.jbi.2023.104526
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发表时间：
2023-10
影响因子：
4.5
通讯作者：
Junde Chen;Trudi Di Qi;Jacqueline Vu;Yuxin Wen
中科院分区：
文献类型：
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作者：
Junde Chen;Trudi Di Qi;Jacqueline Vu;Yuxin Wen
目的准确预测重症监护病房（ICU）的住院时间（LoS）和死亡率是医院有效管理的关键，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。预测ICU住院患者的LoS和死亡率。首先，提出了一种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，一个atrous因果空间金字塔池（SPP）模块被合并到网络中以提取高级特征。优化的焦点损失（FL）函数与合成少数过采样技术（SMOTE）相结合，以减轻不平衡类问题。结果在MIMIC-IV v1.0基准数据集上，所提出的方法实现了LoS预测的最优R-SquareandRMSE值为0.57和3.61，最高检验准确率为97.73%。结论该方法具有上级性能，它可以有效地执行LoS和死亡率预测任务。
PurposeAccurate 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.MethodsThis 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.ResultsOn the MIMIC-IV v1.0 benchmark dataset, the proposed approach achieves the optimumR-SquareandRMSEvalues of 0.57 and 3.61 for the LoS prediction, and the highest test accuracy of 97.73% for the mortality prediction.ConclusionThe 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.