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

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 150960-150968 ◽  
Author(s):  
Tingting Chen ◽  
Jun Xu ◽  
Haochao Ying ◽  
Xiaojun Chen ◽  
Ruiwei Feng ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Extubation Failure ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Gradient Boosting Machine

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

Development of a Diabetes Melitus Detection and Prediction Model Using Light Gradient Boosting Machine and K-Nearest Neighbour

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
B. A Omodunbi
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Hybrid Model ◽  
Learning Model ◽  
Experimental Result ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Machine Learning Model ◽  
Gradient Boosting Machine ◽  
Receiver Operating

Diabetes mellitus is a health disorder that occurs when the blood sugar level becomes extremely high due to body resistance in producing the required amount of insulin. The aliment happens to be among the major causes of death in Nigeria and the world at large. This study was carried out to detect diabetes mellitus by developing a hybrid model that comprises of two machine learning model namely Light Gradient Boosting Machine (LGBM) and K-Nearest Neighbor (KNN). This research is aimed at developing a machine learning model for detecting the occurrence of diabetes in patients. The performance metrics employed in evaluating the finding for this study are Receiver Operating Characteristics (ROC) Curve, Five-fold Cross-validation, precision, and accuracy score. The proposed system had an accuracy of 91% and the area under the Receiver Operating Characteristic Curve was 93%. The experimental result shows that the prediction accuracy of the hybrid model is better than traditional machine learning

scholarly journals Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island

2020 ◽  
Vol 12 (14) ◽  
pp. 2271 ◽  
Author(s):  
Jinwoong Park ◽  
Jihoon Moon ◽  
Seungmin Jung ◽  
Eenjun Hwang
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Jeju Island ◽  
Gradient Boosting ◽  
Probabilistic Forecasting ◽  
Training Time ◽  
Light Gradient ◽  
Proposed Model ◽  
Gradient Boosting Machine ◽  
Time Problem

Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.

Prediction of extubation failure in medical intensive care unit patients

2012 ◽  
Vol 27 (6) ◽  
pp. 571-577 ◽  
Author(s):  
Bernd Saugel ◽  
Philipp Rakette ◽  
Alexander Hapfelmeier ◽  
Caroline Schultheiss ◽  
Veit Phillip ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Medical Intensive Care Unit ◽  
Extubation Failure ◽  
Medical Intensive Care

Prediction of extubation failure in Intensive Care Unit: systematic review of parameters investigated

2019 ◽  
Vol 85 (3) ◽  
Author(s):  
Filomena S. Lombardi ◽  
Antonella Cotoia ◽  
Rocco Petta ◽  
Marcus Schultz ◽  
Gilda Cinnella ◽  
...  
Keyword(s):  
Systematic Review ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Extubation Failure
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