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Extubation failure is a complex and ongoing problem in the intensive care unit (ICU). It refers to the patients who require re-intubation after extubation (namely disconnection from mechanical ventilation). In these patients, extubation failure leads to severe risks associated with re-intubation and is associated with increased mortalities, longer stay in ICU and also higher health care costs. Many studies have been proposed to analyze the problem of extubation failure and identify possible factors or indices that may predict extubation failure. However, these studies used a small number of patients for extubation failure and limited their features to several vital signs or main characteristics. We argue that these are insufficient and less accurate for the prediction of extubation failure. In this paper, we analyze 3636 adult patient records in the MIMIC-III clinical database and apply the Light Gradient Boosting Machine (LightGBM) to predict extubation failure. Also, we perform feature importance analysis according to the result of LightGBM and interpret these features using SHapley Additive exPlanations (SHAP). Experimental results show that our LightGBM method is effective in predicting extubation failure and outperform other machine learning methods such as artificial neural network (ANN), logistic regression (LR) and support vector machine (SVM). The results of feature importance and SHAP analysis are also proved effective and accurate.

Prediction of Extubation Failure for Intensive Care Unit Patients Using Light Gradient Boosting Machine