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• W4309503926 abstract "Acute respiratory distress syndrome (ARDS) is common in intensive care units with high mortality rate and mechanical ventilation (MV) is the most important related treatment. Early prediction of MV duration has benefit for patients risk stratification and care strategies support.To develop an explainable model for predicting mechanical ventilation (MV) duration in patients with ARDS using the machine learning (ML) approach.The number of 1,148, 1,697, and 29 ARDS patients admitted to intensive care units (ICU) in the MIMIC-IV, eICU-CRD, and AmsterdamUMCdb databases were included in the study. Features at MV initiation from the MIMIC-IV dataset were used to train prediction models based on seven supervised machine learning algorithms. After 5-fold cross-validation for hyperparameters tuning, the hyperparameters- optimized model of different algorithms was tested by external datasets extracted from eICU-CRD and Amsterdamumcdb. Finally, three descriptive machine learning explanation methods were conducted for the model explanation.The XGBoosting model showed the most stable and accurate performance among two testing datasets (RMSE= 5.57 and 5.46 days in eICU-CRD and AmsterdamUMCdb) and was selected as the optimal model. The model explanation based on SHAP, LIME, and DALEX results showed a consistent result, vasopressor, PH, and SOFA score had the highest effect on MV duration prediction.ML models with features at MV initiation can accurate predict MV duration in patients with ARDS in ICUs. Among seven algorithms, XGB models showed the best performance (RMSE= 5.57 and 5.46 in two external datasets). LIME, SHAP, and Breakdown methods showed good performance as AXI methods." @default.
• W4309503926 created "2022-11-28" @default.
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• W4309503926 date "2023-03-01" @default.
• W4309503926 modified "2023-10-14" @default.
• W4309503926 title "Developing an explainable machine learning model to predict the mechanical ventilation duration of patients with ARDS in intensive care units" @default.
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• W4309503926 doi "https://doi.org/10.1016/j.hrtlng.2022.11.005" @default.
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