Prediction of traumatic hemorrhagic shock using a Multi-scale exogenous variable model (MS-TimeXer-MoE).

IF 2.2
Wenxin Wang, Bing Chen, Qiuyi Wang, Jian Rong
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Abstract

Objective: To predict the likelihood of hemorrhagic shock in trauma patients using a multi-scale exogenous variable model, MS-TimeXer-MoE.

Methods: This study is the first to use the TimeXer method incorporating exogenous variables to establish a predictive model for traumatic hemorrhagic shock, achieving notable prediction results. Data from trauma patients were extracted from the MIMIC IV database according to inclusion and exclusion criteria. After data processing, the most relevant indicators were selected, including endogenous variables (e.g., vital signs, laboratory indicators) and exogenous variables (e.g., cause of trauma, gender, age, injury site). By integrating exogenous variables and multi-scale feature learning, and innovatively combining the multi-expert mechanism with the TimeXer method, a multi-expert mixed model, MS-TimeXer-MoE, was developed to predict hemorrhagic shock occurrence with higher accuracy and specificity. A total of 4,870 patients were included, divided into an experimental group of 2,432 cases and a control group of 2,438 cases based on the occurrence of hemorrhagic shock post-admission. The dataset was split into training, validation, and testing sets in a 60%:20%:20% ratio RESULTS: The AUC value of the MS-TimeXer-MoE model in predicting hemorrhagic shock in trauma patients was 0.8995, with a recall rate of 0.8607, demonstrating high efficiency in shock identification, indicating that the model can distinguish positive and negative samples with high accuracy and recall rate. In regression tasks, the MS-TimeXer-MoE model's mean absolute error (MAE) was 3.4397, mean squared error (MSE) was 8.2735, mean absolute percentage error (MAPE) was 5.9933%, and the coefficient of determination (R2) reached 87.3436%, showing good fitting and accuracy in time-series data prediction. In the five-fold cross-validation experiment, the variances of MAE and MSE were 0.031 and 0.174, respectively, further reflecting the model's low error fluctuation across different folds, ensuring accurate prediction of shock occurrence time.

Conclusion: Compared to existing mainstream modeling methods for predicting shock occurrence, the MS-TimeXer-MoE model can more accurately predict the occurrence of hemorrhagic shock in trauma patients.

使用多尺度外源性变量模型(ms - timer - moe)预测外伤性失血性休克。
目的:应用多尺度外源变量模型ms - timer - moe预测创伤患者失血性休克发生的可能性。方法:本研究首次采用结合外源变量的TimeXer方法建立外伤性失血性休克的预测模型,预测结果显著。根据纳入和排除标准从MIMIC IV数据库中提取创伤患者的数据。数据处理后,选择最相关的指标,包括内源性变量(如生命体征、实验室指标)和外源性变量(如创伤原因、性别、年龄、损伤部位)。通过整合外源变量和多尺度特征学习,创新地将多专家机制与TimeXer方法相结合,建立了多专家混合模型MS-TimeXer-MoE,预测失血性休克的发生具有更高的准确性和特异性。共纳入4870例患者,根据入院后失血性休克的发生情况分为实验组2432例和对照组2438例。结果:ms - timer - moe模型预测创伤患者失血性休克的AUC值为0.8995,召回率为0.8607,显示出较高的休克识别效率,说明该模型能够区分阳性和阴性样本,准确率和召回率较高。在回归任务中,ms - timer - moe模型的平均绝对误差(MAE)为3.4397,均方误差(MSE)为8.2735,平均绝对百分比误差(MAPE)为5.9933%,决定系数(R2)达到87.3436%,对时间序列数据的预测具有良好的拟合和准确性。在五重交叉验证实验中,MAE和MSE的方差分别为0.031和0.174,进一步反映了模型在不同折叠间的误差波动较小,保证了对冲击发生时间的准确预测。结论:与现有预测休克发生的主流建模方法相比,ms - timer - moe模型能更准确地预测创伤患者失血性休克的发生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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