A predictive model and performance evaluation for acute respiratory distress syndrome in the elderly patients with severe trauma based on interpretable machine learning

Corresponding author: Xu Hua, hzzxyy_006@163.com

DOI: 10.12201/bmr.202602.00094

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  Abstract: Objective To construct a predictive model based on machine learning algorithms for evaluating the risk of acute respiratory distress syndrome (ARDS) in elderly patients with severe trauma. Methods A total of 326 elderly patients with severe trauma admitted to Huzhou Central Hospital from January 2020 to October 2025 were selected as the research subjects. According to a ratio of 7 to 3, they were randomly divided into a modeling group (70%) and a validation group (30%). Based on modeling group data, univariate analysis, lasso regression and multivariate logistic regression were used to analyze the risk factors for ARDS in patients. Four machine learning algorithms, namely random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN) and logistic regression (LR) were used to construct the models. The validation group data was used to evaluate the performance of models. In addition, Shapley additive explanation (SHAP) was used to identify the contribution of risk factors. ResultsBased on modeling group data, a total of 5 feature variables related to ARDS were selected through univariate analysis and lasso regression.Further incorporating multiple logistic regression analysis, the results showed that Glasgow Coma Score (GCS)≤8 points, chestAbbreviated Injury Scale (AIS)≥3 points, White Blood Cell (WBC), Lactic Acid (Lac) and Glucose (Glu) were independent risk factors for ARDS in elderly patients with severe trauma.Based on the above five variables, four machine learning models were constructed, among which the RF model showed the best performance in the modeling group and validation group, AUC were 0.877 and 0.838, F1 scores were 0.750 and 0.557. The calibration curve showed that the RF model had good consistency between the predicted probability and the actual probability in the modeling and validation groups. The decision curve showed that the RF model had a wide clinical net benefit between both groups. In addition, SHAP analysis showed that Glu, Lac and WBC were the top three important feature variables for RF model prediction of ARDS. Conclusion The predictive model for ARDS in elderly patients with severe trauma based on RF model had good predictive ability, and SHAP method improved the interpretability of RF model, which helps to guide clinical practice.

  Key words: Elderly; Severe trauma; Machine learning; Acute respiratory distress syndrome; Predictive model

  Submit time: 24 February 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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