Journal
IEEE ACCESS
Volume 7, Issue -, Pages 150960-150968Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2946980
Keywords
Ventilation; Predictive models; Feature extraction; Diseases; Blood pressure; Blood; MIMICs; Extubation failure prediction; feature importance; light gradient boosting machine; shapley additive explanations
Categories
Funding
- Subject of the Major Commissioned Project Research on China's Image in the Big Data'' of Zhejiang Province's Social Science Planning Advantage Discipline Evaluation and Research on the Present Situation of China's Image'' [16YSXK01ZD-2YB]
- Ministry of Education of China [2017PT18]
- Zhejiang University Education Foundation [K18-511120-004, K17-511120-017, K17-518051-021]
- Major Scientic Project of Zhejiang Laboratory [2018DG0ZX01]
- National Natural Science Foundation of China [61672453]
- Key Laboratory of Medical Neurobiology of Zhejiang Province
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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 SH apley A dditive exP lanations (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.
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