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. 2025 Apr 25:12:1580345.
doi: 10.3389/fmed.2025.1580345. eCollection 2025.

An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome

Wanyi Li #  1 , Hangyu Zhou #  2 , Yingxue Zou  1
Affiliations

An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome

Wanyi Li et al. Front Med (Lausanne). .

Abstract

Background: Acute respiratory distress syndrome (ARDS) is a clinical syndrome triggered by pulmonary or extra-pulmonary factors with high mortality and poor prognosis in the ICU. The aim of this study was to develop an interpretable machine learning predictive model to predict the risk of death in patients with ARDS in the ICU.

Methods: The datasets used in this study were obtained from two independent databases: Medical Information Mart for Intensive Care (MIMIC) IV and eICU Collaborative Research Database (eICU-CRD). This study used eight machine learning algorithms to construct predictive models. Recursive feature elimination with cross-validation is used to screen features, and cross-validation-based Bayesian optimization is used to filter the features used to find the optimal combination of hyperparameters for the model. The Shapley additive explanations (SHAP) method is used to explain the decision-making process of the model.

Results: A total of 5,732 patients with severe ADRS were included in this study for analysis, of which 1,171 patients (20.4%) did not survive. Among the eight models, XGBoost performed the best; AUC-ROC was 0.887 (95% CI: 0.863-0.909) and AUPRC was 0.731 (95% CI: 0.673-0.783).

Conclusion: We developed a machine learning-based model for predicting the risk of death of critically ill ARDS patients in the ICU, and our model can effectively identify high-risk ARDS patients at an early stage, thereby supporting clinical decision-making, facilitating early intervention, and improving patient prognosis.

Keywords: ICU; acute respiratory distress syndrome; machine learning; mortality; prediction model.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the study design.
Figure 2
Figure 2
Performance of machine learning models in predicting ARDS mortality. (A) Receiver operating characteristic curve. (B) Decision curve analysis. (C) Precision–recall curve. (D) Calibration curve.
Figure 3
Figure 3
Subgroup analysis of XGBoost for predicting risk of mortality in patients with ARDS.
Figure 4
Figure 4
Explanation of ARDS mortality risk prediction models. (A) Top 20 risk factors predicted by XGBoost. (B) Top 20 risk factors for the SHAP. (C) A nonsurviving patient and a surviving patient. (D) SHAP dependence plot.
See this image and copyright information in PMC

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