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

Chen, Tingting; Xu, Jun; Ying, Haochao; Chen, Xiaojun; Feng, Ruiwei; Fang, Xianyong; Gao, Honghao; Wu, Jian

doi:10.1109/access.2019.2946980

Cited by 80 publications

(59 citation statements)

References 29 publications

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“…In this study, LightGBM had the highest AUC value among the three models. This is consistent with the study by Chen et al, which showed that, compared with XGBoost, artificial neural network, and support vector machine, LightGBM was the most effective model to predict extubation 22 . In the evaluation metrics, high precision decreases reintubation, and high recall decreases unnecessary ventilator use and tracheostomy.…”

Section: Discussionsupporting

confidence: 92%

“…Machine learning has the potential to improve the prediction of successful extubation. Although there are numerous studies on mechanical ventilation, only a few studies use machine learning to predict the success of weaning from ventilatory support [22][23][24] . Thus, this study aimed to investigate the performance and accuracy of machine learning to predict the success of extubation.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Prediction of Successful Extubation of Mechanical Ventilated Patients in an Intensive Care Unit: A Retrospective Observational Study

et al. 2021

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Background: Ventilator weaning protocols are commonly implemented for patients receiving mechanical ventilation. However, the rate of extubation failure remains high despite the protocols. This study investigated the usefulness and accuracy of ventilator weaning through machine learning to predict successful extubation. Methods: We retrospectively evaluated the data of patients who underwent intubation for respiratory failure and received mechanical ventilation in the intensive care unit (ICU). Data on 57 factors including patient demographics, vital signs, laboratory data, and data from ventilator were extracted. Extubation failure was defined as re-intubation within 72 hours of extubation. For supervised learning, the data were labeled requirement of intubation or not. We used three learning algorithms (Random Forest, XGBoost, and LightGBM) to predict successful extubation. We also analyzed important features and evaluated the area under curve (AUC) and prediction metrics. Results: Overall, 13 of the 117 included patients required re-intubation. LightGBM had the highest AUC (0.950), followed by XGBoost (0.946) and Random Forest (0.930). The accuracy, precision, and recall performance were 0.897, 0.910, and 4 0.909, for Random Forest; 0.910, 0.912, and 0.931 for XGBoost; and 0.927, 0.915, and 0.960 for LightGBM, respectively. The most important feature was the duration of mechanical ventilation followed by the fraction of inspired oxygen, positive end-expiratory pressure, maximum and mean airway pressures, and Glasgow Coma Scale. Conclusions: Machine learning could predict successful extubation among patients on mechanical ventilation in the ICU. LightGBM has the highest overall performance. The duration of mechanical ventilation was the most important feature in all models.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Prediction of Successful Extubation of Mechanical Ventilated Patients in an Intensive Care Unit: A Retrospective Observational Study

et al. 2021

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“…Namely, the specificity, sensitivity, accuracy, F1 score and error rate metrics of the proposed strategy were assessed using the following formulas, where TP stands for the number of true positives, TN stands for the number of true negatives and FP and FN denote the numbers of first and second error types (false positives and false negatives, respectively) ( Chen et al, 2019 ).…”

Section: Experimental Verification and Resultsmentioning

confidence: 99%

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

Bodzas

Kodytek

Žídek

2020

Front. Bioeng. Biotechnol.

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Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively.

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“…In this section, discussions of existing techniques are presented, which are used to predict the health status of patients using machine learning techniques. In addition, the advantages and limitations of the existing methods were also discussed in [13][14][15][16][17][18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…T. Chen, J. Xu, H. Ying, X. Chen, R. Feng, X. Fang, and J. Wu, [17] predicted the Extubation Failure (EF) by analyzing 3636 adult patient records in MIMIC-III clinical database using Light Gradient Boosting Machine (LightGBM). According to the results of LightGBM, afeature importance analysis were carried out by interpreting these features using SHapley Additive exPlanations (SHAP).…”

Section: Literature Reviewmentioning

confidence: 99%

The Prediction of Diseases using Rough Set Theory with Recurrent Neural Network in Big Data Analytics

Talasila¹,

Madhubabu²,

Mahadasyam³

et al. 2020

IJIES

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In a modern life, early healthcare prediction plays an important role to prevent the loss of life caused by prediction delays in treatment. Nowadays, the researchers focused on the Big data analysis, which is used to identify the future health status and provides an efficient way to overcome the issues in early prediction. Many researches are going on predictive analytics using machine learning techniques to provide a better decision making. Big data analysis provides great opportunities to predict future health status from health parameters and provide best outcomes. However, the data classification is one of the major challenging tasks due to noisy data or missing data in the dataset. Feature selection techniques play an important role in the classification process by removing irrelevant features from the extracted data. In this research work, the Rough Set Theory (RST) technique is used to select the most relevant features, which helps to provide the efficient classification of medical data and disease detection. The selected features are given as input to the Recurrent Neural Network (RNN) technique for disease prediction. The proposed method is also called as RST-RNN, where the experiments are carried out on the UCI machine learning repository dataset in terms of accuracy, f-measure, sensitivity and specificity. The results showed that the RST-RNN method achieved accuracy of 98.57%, where the existing Support Vector Machine (SVM) achieved 90.57% accuracy and Naive Bayes (NB) achieved 97.36% accuracy for heart disease dataset.

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

Cited by 80 publications

References 29 publications

Machine Learning for Prediction of Successful Extubation of Mechanical Ventilated Patients in an Intensive Care Unit: A Retrospective Observational Study

Machine Learning for Prediction of Successful Extubation of Mechanical Ventilated Patients in an Intensive Care Unit: A Retrospective Observational Study

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

The Prediction of Diseases using Rough Set Theory with Recurrent Neural Network in Big Data Analytics

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