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

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, ach...

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Published in:European Journal of Trauma & Emergency Surgery Vol. 51; no. 1; pp. 1 - 13
Main Authors: Wang, Wenxin, Chen, Bing, Wang, Qiuyi, Rong, Jian
Format: research tables/charts Journal Article
Published: Springer Nature 6/5/2025
Online Access:View this record in EBSCOhost
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      dt: 6/5/2025
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s00068-025-02878-8
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        atl: Prediction of traumatic hemorrhagic shock using a Multi-scale exogenous variable model (MS-TimeXer-MoE).
      aug:
        au:
          Wang, Wenxin
          Chen, Bing
          Wang, Qiuyi
          Rong, Jian
        affil: https://ror.org/03rc99w60 The Second Hospital of Tianjin Medical University, 300211, Tianjin, China
      sug:
        subj:
          Emergency Patients
          Shock, Traumatic Risk Factors
          Shock, Hemorrhagic Risk Factors
          Risk Assessment
          Prediction Models Evaluation
          Human
          Male
          Female
          Vital Signs
          Trauma Etiology
          Sex Factors
          Age Factors
          Sensitivity and Specificity
          Validity
          ROC Curve
          Regression
          Male
          Female
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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