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Real-time monitoring of in-hospital mortality risk in intensive care units heart failure patients using an extreme gradient boosting model 认领 引用
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作者 Yu-Juan XIONG Si-Si LING +5 位作者 Wen-Tao HU Xin-Yu WEN Xiu-Miao ZHENG Wei-Hua LIU Wen-Chao OU Ben-Rong LIU 《Journal of Geriatric Cardiology》 SCIE CAS CSCD 2026年第3期184-205,共22页
Objectives To develop and validate a machine learning(ML)model for real-time monitoring of in-hospital mortality(IHM)risk and identification of major risk factors in heart failure(HF)patients admitted to intensive car... Objectives To develop and validate a machine learning(ML)model for real-time monitoring of in-hospital mortality(IHM)risk and identification of major risk factors in heart failure(HF)patients admitted to intensive care units(ICUs).Methods Data from ICU HF patients were extracted from the multicenter eICU-Collaborative Research Database(eICU-CRD.External validation used MIMIC-IV and a real-world Chinese dataset(CHN-dataset).Daily measurements from MIMIC-IV patients staying≥3 days formed a Daily Measurement(DM)dataset.After rigorous preprocessing and feature selection,five ML algorithms were trained and optimized using eICU-CRD data.Model performance was evaluated using AUC,sensitivity,specificity,and balanced accuracy.The optimal model was benchmarked against APACHE and SOFA scores.SHapley Additive ex-Planations(SHAP)interpreted feature contributions.A Windows application was developed for clinical deployment.Results XGBoost emerged as the optimal model(Final-ML model),which incorporated only 17 routinely collected clinical variables:age,non-invasive systolic blood pressure(NI-SBP),heart rate,respiratory rate,Glasgow Coma Scale eye opening score,white blood cell count(WBC),creatinine,bicarbonate,red cell distribution width(RDW),platelet count,glucose,calcium,mean corpuscular hemoglobin concentration(MCHC),sodium,mean corpuscular volume,red blood cell count,and potassium.It achieved high AUCs:0.876(95%CI:0.836-0.915;eICU-CRD test data),0.932(95%CI:0.921-0.942;MIMIC-IV),and 0.879(95%CI:0.846-0.912;CHN-dataset).It significantly outperformed APACHE(AUC=0.740,95%CI:0.720-0.761)and SOFA(AUC=0.717,95%CI:0.694-0.740)scores.The model demonstrated strong generalizability across ethnicities,ward types,and genders within MIMIC-IV.Using daily data(DM dataset),predicted IHM risk accurately tracked patient trajectories:risk decreased progressively for survivors and increased for non-survivors throughout the ICU stay.SHAP analysis identified key predictors:NI-SBP,age,heart rate,WBC,glucose,and notably,RDW and MCHC.Time-dependent Cox regression confirmed RDW increase(HR=3.783,95%CI:2.237-6.398)and MCHC decrease(HR=0.173,95%CI:0.040-0.741)as significant independent risk factors for IHM.Conclusions The developed XGBoost model provides a reliable,generalizable tool for real-time IHM risk quantification and monitoring in ICU HF patients,using only 17 routinely collected clinical variables.It surpasses traditional scoring systems and enables dynamic risk assessment throughout the ICU stay.By identifying patient-specific major risk factors via SHAP values,the model facilitates timely,personalized treatment adjustments. 展开更多
关键词 hospital mortality real time monitoring heart failure extreme gradient boosting identification major risk factors machine learning intensive care units icus methods feature selection
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Spatiotemporal dynamics of land surface temperature in Yunnan,China:Interpreting multi-temporal drivers using eXtreme Gradient Boosting(XGBoost)and SHapley Additive exPlanations(SHAP) 认领 引用
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作者 LIAO Chaolian HE Xudong +3 位作者 LONG Xiaomin WAN Ailing ZHOU Ruliang WANG Yanxia 《Regional Sustainability》 CAS CSCD 2026年第3期53-73,共21页
Amid global warming,mountainous regions have emerged as critical zones of investigation owing to their heightened vulnerability to climate change,their ecological significance,and the intensified interactions between ... Amid global warming,mountainous regions have emerged as critical zones of investigation owing to their heightened vulnerability to climate change,their ecological significance,and the intensified interactions between natural stress and human activities.Land surface temperature(LST)is a fundamental indicator for assessing climatic sensitivity in these landscapes.However,a comprehensive understanding of the spatiotemporal dynamics and driving mechanisms of LST across large mountainous regions remains limited.Therefore,data from the Terra Moderate Resolution Imaging Spectroradiometer Land Surface Temperature/Emissivity Daily(MOD11A1)Version 6.1 product during 2001–2020 in Yunnan Province(a complex mountainous region),China,were analyzed.Sen's slope analysis and Mann-Kendall test were applied to detect LST trends and spatial heterogeneity at both annual and seasonal scales.Subsequently,an eXtreme Gradient Boosting(XGBoost)model coupled with SHapley Additive exPlanations(SHAP)was employed to clarify the nonlinear contributions of multiple drivers.The study revealed the following findings.LST exhibited an overall warming rate of 0.020℃/a,characterized by daytime cooling(–0.008℃/a)and nighttime warming(0.048℃/a).LST increased during spring,summer,and autumn(0.011℃/a–0.018℃/a),whereas winter LST exhibited a cooling trend(–0.011℃/a).These variations were spatially partitioned by the Ailao Mountains,with the southwest displaying stronger thermal changes than the northeast.Natural controls,including digital elevation model(DEM)and downward shortwave radiation(DSR),predominated in the northwest high mountain canyons area and south tropical rainforest area,whereas nature–human interactions were more pronounced in the central urban agglomeration warming area and southeast karst landform area.The dominant drivers consisted of DEM,DSR,Normalized Difference Moisture Index,particulate matter 2.5(PM2.5),and aerosol optical depth(AOD).The strong correlations between gross domestic product and population density(correlation coefficient(r)=0.95),as well as between PM2.5 and AOD(r=0.84),highlighted the increasing influence of socioeconomic factors on surface warming.This study can advance the understanding of how mountain topography,moisture,and anthropogenic pressures jointly regulate surface thermal regimes and provide region-specific insights for climate adaptation and sustainable ecosystem management. 展开更多
关键词 Land surface temperature(LST) Sen’s slope analysis Mann-Kendall test Driving mechanism eXtreme Gradient Boosting(XGBoost) SHapley Additive exPlanations(SHAP) Yunnan
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Extreme gradient boosting with Shapley Additive Explanations for landslide susceptibility at slope unit and hydrological response unit scales 认领 引用 被引量：1
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作者 Ananta Man Singh Pradhan Pramit Ghimire +3 位作者 Suchita Shrestha Ji-Sung Lee Jung-Hyun Lee Hyuck-Jin Park 《Geoscience Frontiers》 SCIE CAS CSCD 2025年第4期357-372,共16页
This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging... This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings. 展开更多
关键词 Landslide susceptibility mapping Hydrological response units Slope units Extreme gradient boosting Hyper parameter tuning Shapley additive explanations
Noninvasive prediction of esophagogastric varices in hepatitis B:An extreme gradient boosting model based on ultrasound and serology 认领 引用 被引量：1
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作者 Si-Yi Feng Zong-Ren Ding +1 位作者 Jin Cheng Hai-Bin Tu 《World Journal of Gastroenterology》 SCIE CAS 2025年第13期62-78,共17页
BACKGROUND Severe esophagogastric varices(EGVs)significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage.Endoscopy is the gold standard for EGV detection but it is ... BACKGROUND Severe esophagogastric varices(EGVs)significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage.Endoscopy is the gold standard for EGV detection but it is invasive,costly and carries risks.Noninvasive predictive models using ultrasound and serological markers are essential for identifying high-risk patients and optimizing endoscopy utilization.Machine learning(ML)offers a powerful approach to analyze complex clinical data and improve predictive accuracy.This study hypothesized that ML models,utilizing noninvasive ultrasound and serological markers,can accurately predict the risk of EGVs in hepatitis B patients,thereby improving clinical decisionmaking.AIM To construct and validate a noninvasive predictive model using ML for EGVs in hepatitis B patients.METHODS We retrospectively collected ultrasound and serological data from 310 eligible cases,randomly dividing them into training(80%)and validation(20%)groups.Eleven ML algorithms were used to build predictive models.The performance of the models was evaluated using the area under the curve and decision curve analysis.The best-performing model was further analyzed using SHapley Additive exPlanation to interpret feature importance.RESULTS Among the 310 patients,124 were identified as high-risk for EGVs.The extreme gradient boosting model demonstrated the best performance,achieving an area under the curve of 0.96 in the validation set.The model also exhibited high sensitivity(78%),specificity(94%),positive predictive value(84%),negative predictive value(88%),F1 score(83%),and overall accuracy(86%).The top four predictive variables were albumin,prothrombin time,portal vein flow velocity and spleen stiffness.A web-based version of the model was developed for clinical use,providing real-time predictions for high-risk patients.CONCLUSION We identified an efficient noninvasive predictive model using extreme gradient boosting for EGVs among hepatitis B patients.The model,presented as a web application,has potential for screening high-risk EGV patients and can aid clinicians in optimizing the use of endoscopy. 展开更多
关键词 Esophagogastric varices Machine learning Extreme gradient boosting Ultrasound Serological markers
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Predictive model and risk analysis for outcomes in diabetic foot ulcer using eXtreme Gradient Boosting algorithm and SHapley Additive exPlanation 认领 引用 被引量：1
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作者 Lei Gao Zi-Xuan Liu Jiang-Ning Wang 《World Journal of Diabetes》 SCIE 2025年第7期167-183,共17页
BACKGROUND Diabetic foot ulcer(DFU)is a serious and destructive complication of diabetes,which has a high amputation rate and carries a huge social burden.Early detection of risk factors and intervention are essential... BACKGROUND Diabetic foot ulcer(DFU)is a serious and destructive complication of diabetes,which has a high amputation rate and carries a huge social burden.Early detection of risk factors and intervention are essential to reduce amputation rates.With the development of artificial intelligence technology,efficient interpretable predictive models can be generated in clinical practice to improve DFU care.AIM To develop and validate an interpretable model for predicting amputation risk in DFU patients.METHODS This retrospective study collected basic data from 599 patients with DFU in Beijing Shijitan Hospital between January 2015 and June 2024.The data set was randomly divided into a training set and test set with fivefold cross-validation.Three binary variable models were built with the eXtreme Gradient Boosting(XGBoost)algorithm to input risk factors that predict amputation probability.The model performance was optimized by adjusting the super parameters.The pre-dictive performance of the three models was expressed by sensitivity,specificity,positive predictive value,negative predictive value and area under the curve(AUC).Visualization of the prediction results was realized through SHapley Additive exPlanation(SHAP).RESULTS A total of 157(26.2%)patients underwent minor amputation during hospitalization and 50(8.3%)had major amputation.All three XGBoost models demonstrated good discriminative ability,with AUC values>0.7.The model for predicting major amputation achieved the highest performance[AUC=0.977,95%confidence interval(CI):0.956-0.998],followed by the minor amputation model(AUC=0.800,95%CI:0.762-0.838)and the non-amputation model(AUC=0.772,95%CI:0.730-0.814).Feature importance ranking of the three models revealed the risk factors for minor and major amputation.Wagner grade 4/5,osteomyelitis,and high C-reactive protein were all considered important predictive variables.CONCLUSION XGBoost effectively predicts diabetic foot amputation risk and provides interpretable insights to support person-alized treatment decisions. 展开更多
关键词 Diabetic foot ulcer Amputation risk stratification Clinical risk prediction eXtreme Gradient Boosting SHapley Additive exPlanation Machine learning
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Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization 认领 引用 被引量：124
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作者 Wengang Zhang Chongzhi Wu +2 位作者 Haiyi Zhong Yongqin Li Lin Wang 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第1期469-477,共9页
Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random fo... Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random forest(RF)ensemble learning methods for capturing the relationships between the USS and various basic soil parameters.Based on the soil data sets from TC304 database,a general approach is developed to predict the USS of soft clays using the two machine learning methods above,where five feature variables including the preconsolidation stress(PS),vertical effective stress(VES),liquid limit(LL),plastic limit(PL)and natural water content(W)are adopted.To reduce the dependence on the rule of thumb and inefficient brute-force search,the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF.The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation(CV).It is shown that XGBoost-based and RF-based methods outperform these approaches.Besides,the XGBoostbased model provides feature importance ranks,which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model. 展开更多
关键词 Undrained shear strength Extreme gradient boosting Random forest Bayesian optimization k-fold CV
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Bridge damage identification based on convolutional autoencoders and extreme gradient boosting trees 认领 引用 被引量：7
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作者 Duan Yuanfeng Duan Zhengteng +1 位作者 Zhang Hongmei Cheng J.J.Roger 《Journal of Southeast University(English Edition)》 EI CAS 2024年第3期221-229,共9页
To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the accele... To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios. 展开更多
关键词 structural health monitoring damage identification convolutional autoencoder(CAE) extreme gradient boosting tree(XGBoost) machine learning
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Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm 认领 引用 被引量：2
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作者 Xianghui Lu Junliang Fan +1 位作者 Lifeng Wu Jianhua Dong 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第11期699-723,共25页
It is important for regional water resources management to know the agricultural water consumption information several months in advance.Forecasting reference evapotranspiration(ET0)in the next few months is import... It is important for regional water resources management to know the agricultural water consumption information several months in advance.Forecasting reference evapotranspiration(ET0)in the next few months is important for irrigation and reservoir management.Studies on forecasting of multiple-month ahead ET0 using machine learning models have not been reported yet.Besides,machine learning models such as the XGBoost model has multiple parameters that need to be tuned,and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution.This study investigated the performance of the hybrid extreme gradient boosting(XGBoost)model coupled with the Grey Wolf Optimizer(GWO)algorithm for forecasting multi-step ahead ET0(1-3 months ahead),compared with three conventional machine learning models,i.e.,standalone XGBoost,multi-layer perceptron(MLP)and M5 model tree(M5)models in the subtropical zone of China.The results showed that theGWO-XGB model generally performed better than the other three machine learning models in forecasting 1-3 months ahead ET0,followed by the XGB,M5 and MLP models with very small differences among the three models.The GWO-XGB model performed best in autumn,while the MLP model performed slightly better than the other three models in summer.It is thus suggested to apply the MLP model for ET0 forecasting in summer but use the GWO-XGB model in other seasons. 展开更多
关键词 Reference evapotranspiration extreme gradient boosting Grey Wolf Optimizer multi-layer perceptron M5 model tree
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Modeling of Total Dissolved Solids (TDS) and Sodium Absorption Ratio (SAR) in the Edwards-Trinity Plateau and Ogallala Aquifers in the Midland-Odessa Region Using Random Forest Regression and eXtreme Gradient Boosting 认领 引用
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作者 Azuka I. Udeh Osayamen J. Imarhiagbe Erepamo J. Omietimi 《Journal of Geoscience and Environment Protection》 2024年第5期218-241,共24页
Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. ... Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R2 values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large. 展开更多
关键词 Water Quality Prediction Predictive Modeling Aquifers Machine Learning Regression eXtreme Gradient Boosting
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Object-Based Burned Area Mapping with Extreme Gradient Boosting Using Sentinel-2 Imagery 认领 引用
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作者 Dimitris Stavrakoudis Ioannis Z. Gitas 《Journal of Geographic Information System》 2023年第1期53-72,共20页
The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper ... The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93. 展开更多
关键词 Operational Burned Area Mapping Sentinel-2 Extreme Gradient Boosting (XGB) QuickShift Segmentation Machine Learning
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Improving nuclear mass predictions by correcting mass residuals using eXtreme Gradient Boosting 认领 引用
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作者 X.Y.Zhang W.F.Li J.Y.Fang 《Chinese Physics C》 SCIE CAS CSCD 2026年第4期203-210,共8页
Nuclear masses are investigated for the first time using the eXtreme Gradient Boosting(XGBoost)method.Nucleon numbers,valence nucleon numbers,and physical quantities related to the magic number are used as input featu... Nuclear masses are investigated for the first time using the eXtreme Gradient Boosting(XGBoost)method.Nucleon numbers,valence nucleon numbers,and physical quantities related to the magic number are used as input features for the decision tree,which learns the residuals of experimental binding energies with respect to the Bethe-Weizsäcker(BW2)formula predictions,and the XGBoost method can achieve high accuracy predictions of nuclear binding energy.For nuclear masses of magic number nuclei with prediction challenges,XGBoost can better capture the physical information associated with the magic number compared to that using BW2,and the root mean square deviation of its predicted nuclear mass ranges from 2.769 to 0.732 MeV.Comparing the results of BW2*and XGBoost* with the pseudo-experimental data of Finite-Range Droplet Model(FRDM12)suggests that the XGBoost* method may have better extrapolation abilities. 展开更多
关键词 nuclear mass machine learning eXtreme Gradient Boosting
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Remaining mileage estimation for electric vehicles based on dual extended Kalman filter and eXtreme gradient boosting 认领 引用
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作者 Zhiqiang Han Zeyu Chen +2 位作者 Zhou Yang Zilu Zhang Bo Zhang 《Green Energy and Intelligent Transportation》 EI CSCD 2025年第6期126-138,共13页
Precisely estimating the remaining mileage of electric vehicles is highly important for vehicle control and battery recharging determinations.Remaining mileage estimation(RME)is a technique difficulty in practice sinc... Precisely estimating the remaining mileage of electric vehicles is highly important for vehicle control and battery recharging determinations.Remaining mileage estimation(RME)is a technique difficulty in practice since it is impacted by many factors,including the battery state of charge(SOC),state of health(SOH),ambient temperature,and traffic condition,etc.In this study,an online RME method is proposed based on dual extended Kalman filter(DEKF)and extreme gradient boosting(XGB)algorithms.Firstly,the battery SOC and SOH are co-estimated based on DEKF with considering the impacts of ambient temperature.Secondly,the current traffic condition are analyzed by using a historical data segement,and then the energy consumpation rate is predicted by XGB algorithm.The XGB algorithm's accuracy under the varying length of data segment is analyzed for determining the proper algorithm parameters.The presented method is evaluated by a simulation study.The results under several typical driving cycles indicate that the precise RME can be achieved with the maximum error less than 1.2%.The method is expected to be useful in providing credible mileage estimation in electric vehiecle applications. 展开更多
关键词 Electric vehicle Remaining mileage estimation State of charge estimation Kalman filter Machine learning eXtreme gradient boosting
Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique 认领 引用 被引量：4
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作者 Enming LI Ning ZHANG +2 位作者 Bin XI Jian ZHOU Xiaofeng GAO 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2023年第9期1310-1325,共16页
Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO2)emissions and energy consumption.Therefore,developing waste-substitutable concrete components is nece... Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO2)emissions and energy consumption.Therefore,developing waste-substitutable concrete components is necessary.Improving the sustainability and greenness of concrete is the focus of this research.In this regard,899 data points were collected from existing studies where cement,slag,fly ash,superplasticizer,coarse aggregate,and fine aggregate were considered potential influential factors.The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult.Instead of the traditional compressive strength test,this study combines five novel metaheuristic algorithms with extreme gradient boosting(XGB)to predict the compressive strength of green concrete based on fly ash and blast furnace slag.The intelligent prediction models were assessed using the root mean square error(RMSE),coefficient of determination(R2),mean absolute error(MAE),and variance accounted for(VAF).The results indicated that the squirrel search algorithm-extreme gradient boosting(SSA-XGB)yielded the best overall prediction performance with R2 values of 0.9930 and 0.9576,VAF values of 99.30 and 95.79,MAE values of 0.52 and 2.50,RMSE of 1.34 and 3.31 for the training and testing sets,respectively.The remaining five prediction methods yield promising results.Therefore,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete.Finally,the developed SSA-XGB considered the effects of all the input factors on the compressive strength.The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy. 展开更多
关键词 sustainable concrete fly ash slay extreme gradient boosting technique squirrel search algorithm parametric analysis
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Machine learning using the extreme gradient boosting(XGBoost)algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients 认领 引用 被引量：2
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作者 Jonathan Montomoli Luca Romeo +14 位作者 Sara Moccia Michele Bernardini Lucia Migliorelli Daniele Berardini Abele Donati Andrea Carsetti Maria Grazia Bocci Pedro David Wendel Garcia Thierry Fumeaux Philippe Guerci Reto Andreas Schüpbach Can Ince Emanuele Frontoni Matthias Peter Hilty RISC-19-ICU Investigators 《Journal of Intensive Medicine》 2021年第2期110-116,共7页
Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient surviv... Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient survival probability.Machine learning(ML)techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care.Methods:We retrieved data on patients with COVID-19 admitted to an intensive care unit(ICU)between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit(RISC-19-ICU)registry.We applied the Extreme Gradient Boosting(XGBoost)algorithm to the data to predict as a binary out-come the increase or decrease in patients’Sequential Organ Failure Assessment(SOFA)score on day 5 after ICU admission.The model was iteratively cross-validated in different subsets of the study cohort.Results:The final study population consisted of 675 patients.The XGBoost model correctly predicted a decrease in SOFA score in 320/385(83%)critically ill COVID-19 patients,and an increase in the score in 210/290(72%)patients.The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model(0.86 vs.0.69,P<0.01[paired t-test with 95%confidence interval]).Conclusions:The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems(CDSSs)aimed at optimizing available resources. 展开更多
关键词 Machine learning Extreme gradient boosting(XGBoost) COVID-19 Multiple organ failure Clinical decision support system(CDSS) Organ dysfunction score
Estimation of the axial capacity of high-strength concrete-filled steel tube columns using artificial neural network,random forest,and extreme gradient boosting approaches 认领 引用 被引量：1
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作者 Payam SARIR Anat RUANGRASSAMEE Mitsuyasu IWANAMI 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2024年第11期1794-1814,共21页
The study aims to develop machine learning-based mechanisms that can accurately predict the axial capacity of high-strength concrete-filled steel tube(CFST)columns.Precisely predicting the axial capacity of a CFST col... The study aims to develop machine learning-based mechanisms that can accurately predict the axial capacity of high-strength concrete-filled steel tube(CFST)columns.Precisely predicting the axial capacity of a CFST column is always challenging for engineers.Using artificial neural networks(ANNs),random forest(RF),and extreme gradient boosting(XG-Boost),a total of 165 experimental data sets were analyzed.The selected input parameters included the steel tensile strength,concrete compressive strength,tube diameter,tube thickness,and column length.The results indicated that the ANN and RF demonstrated a coefficient of determination(R2)value of 0.965 and 0.952 during the training and 0.923 and 0.793 during the testing phase.The most effective technique was the XG-Boost due to its high efficiency,optimizing the gradient boosting,capturing complex patterns,and incorporating regularization to prevent overfitting.The outstanding R2 values of 0.991 and 0.946 during the training and testing were achieved.Due to flexibility in model hyperparameter tuning and customization options,the XG-Boost model demonstrated the lowest values of root mean square error and mean absolute error compared to the other methods.According to the findings,the diameter of CFST columns has the greatest impact on the output,while the column length has the least influence on the ultimate bearing capacity. 展开更多
关键词 artificial neural network extreme gradient boosting random forest concrete-filled steel tube machine learning
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Application of extreme gradient boosting in predicting the viscoelastic characteristics of graphene oxide modified asphalt at medium and high temperatures 认领 引用
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作者 Huong-Giang Thi HOANG Hai-Van Thi MAI +1 位作者 Hoang Long NGUYEN Hai-Bang LY 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2024年第6期899-917,共19页
Complex modulus(G*)is one of the important criteria for asphalt classification according to AASHTO M320-10,and is often used to predict the linear viscoelastic behavior of asphalt binders.In addition,phase angle(φ... Complex modulus(G*)is one of the important criteria for asphalt classification according to AASHTO M320-10,and is often used to predict the linear viscoelastic behavior of asphalt binders.In addition,phase angle(φ)characterizes the deformation resilience of asphalt and is used to assess the ratio between the viscous and elastic components.It is thus important to quickly and accurately estimate these two indicators.The purpose of this investigation is to construct an extreme gradient boosting(XGB)model to predict G*andφof graphene oxide(GO)modified asphaltat medium and high temperatures.Two data sets are gathered from previously published experiments,consisting of 357 samples for G*and 339 samples forφ,and the se are used to develop the XGB model using nine inputs representing theasphalt binder components.The findings show that XGB is an excellent predictor of G*andφof GO-modified asphalt,evaluated by the coefficient of determination R2(R2=0.990 and 0.9903 for G*andφ,respectively)and root mean square error(RMSE=31.499 and 1.08 for G*andφ,respectively).In addition,the model’s performance is compared with experimental results and five other machine learning(ML)models to highlight its accuracy.In the final step,the Shapley additive explanations(SHAP)value analysis is conducted to assess the impact of each input and the correlation between pairs of important features on asphalt’s two physical properties. 展开更多
关键词 complex modulus phase angle graphene oxide asphalt extreme gradient boosting machine learning
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Adoption of tree-based machine learning algorithms and CPT data for liquefaction potential assessment 认领 引用 被引量：1
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作者 Tian Shuang Si Pan +2 位作者 Tang Liang Ling Xianzhang Liu Yanfang 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第2期347-360,共14页
Soil liquefaction under strong earthquakes is the primary cause of damage to foundations and superstructures.Predicting the potential for seismic-induced soil liquefaction is key to preventing related disasters.This s... Soil liquefaction under strong earthquakes is the primary cause of damage to foundations and superstructures.Predicting the potential for seismic-induced soil liquefaction is key to preventing related disasters.This study compares four machine learning(ML)models for soil liquefaction potential based on cone penetration test datasets:decision tree,random forest,gradient boosting,and extreme gradient boosting.The database was collected from previously published research and includes information on earthquake moment magnitude,peak ground acceleration,depth of soil layer,total vertical stresses,effective vertical stresses,and cone tip stresses.The predictive capabilities of the developed models were evaluated using overall accuracy,precision,recall,F-measure,and receiver operating characteristic curves.The results showed that the extreme gradient boosting model exhibited the highest efficacy.A subsequent analysis of feature importance demonstrated that cone tip stresses exerted the most significant influence on soil liquefaction potential.In a final comparative assessment with conventional liquefaction discrimination theory methods,the study revealed that the Robertson method yielded a higher success rate for liquefaction cases,the Olsen method was more successful in non-liquefaction cases,and the ML approach manifested superior success rates in both liquefaction and non-liquefaction cases. 展开更多
关键词 liquefaction potential cone penetration test machine learning extreme gradient boosting
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Real-time safety control of shield attitude considering tunneling efficiency 认领 引用
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作者 Tugen FENG Jinjian HU +3 位作者 Jian ZHANG Guoping REN Yongbo LI Xiaopeng ZHAO 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第1期80-95,共16页
Shield attitude control is a critical aspect that must be continuously monitored during shield tunneling.To achieve scientifically rational settings for shield tunneling parameters,this study constructed multiple mach... Shield attitude control is a critical aspect that must be continuously monitored during shield tunneling.To achieve scientifically rational settings for shield tunneling parameters,this study constructed multiple machine learning prediction models,including shield attitude deviations and tunneling speed,and optimized the hyperparameters of these models using Bayesian algorithms.Subsequently,a constrained grey wolf optimization(GWO)algorithm was employed to establish a real-time safety control method for attitude that considers tunneling efficiency,by dynamically updating the upper and lower bounds for adjustable parameters.The results indicate that the k-nearest neighbors(KNN)model achieved the highest prediction accuracy;however,due to its specific algorithmic principles,KNN is unsuitable for optimization tasks.Embedding the extreme gradient boosting model into the GWO algorithm yielded the best attitude control performance:the absolute attitude deviations were reduced by an average of 45.1%compared to actual values,while the rate of change for adjustable parameters did not exceed 30%.This approach ensures safety and tunneling efficiency during attitude correction and exhibits universal applicability.Compared with other optimization algorithms,GWO demonstrated significant advantages in both optimization effectiveness and computational time. 展开更多
关键词 shield attitude tunneling speed K-nearest neighbors support vector regression extreme gradient boosting grey wolf optimization
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Prediction of mixed-modeⅠ/Ⅱfracture toughness of rock-concrete bi-material disc with interface crack:Interpretable NRBO-XGBoost-SHAP model and experimental validation 认领 引用
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作者 Tengfei Guo Congxiang Yuan +3 位作者 Xu Chang Zhijun Zhang Guicheng He Yichao Rui 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第7期1453-1473,共21页
Accurately determining the effective fracture toughness(Keff)of rock-concrete(R-C)bi-materials,governed by interface inclination and ambient temperature,is a prerequisite for assessing their structural stability.Th... Accurately determining the effective fracture toughness(Keff)of rock-concrete(R-C)bi-materials,governed by interface inclination and ambient temperature,is a prerequisite for assessing their structural stability.This study developed a hybrid NRBO-XGBoost prediction model using the Newton-RaphsonBased Optimizer(NRBO)to tune the hyperparameters of Extreme Gradient Boosting(XGBoost)model.The established model was developed based on 154 datasets obtained from laboratory tests and numerical simulations with the cracked straight-through Brazilian disc(CSTBD)specimens,including twelve input parameters.The NRBO-XGBoost model for Keffprediction was investigated and compared with seven more models.Furthermore,the Shapley Additive exPlanations(SHAP)method was employed to quantify the contributions of inputs to Keffto improve the interpretability of the developed model.Finally,new data were used to validate the model.Evaluation results demonstrate that metaheuristic optimization algorithms significantly enhance the performance of XGBoost,with NRBO-XGBoost performing the best.The models rank from highest to lowest prediction performance as follows:NRBO-XGBoost,WOA-XGBoost,PSO-XGBoost,XGBoost,RF,CatBoost,LightGBM,and AdaBoost.The interpretable analysis shows that the interface inclination angle exerts the dominant influence.The validation results demonstrate that NRBO-XGBoost achieves high predictive accuracy on a new dataset,showing promising implications for practical applications. 展开更多
关键词 Effective fracture toughness R-C bi-material Extreme Gradient Boosting Newton-Raphson-Based Optimizer SHAP
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A novel approach to identify the spatial characteristics of ozone-precursor sensitivity based on interpretable machine learning 认领 引用
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作者 Huiling He Kaihui Zhao +6 位作者 Zibing Yuan Jin Shen Yujun Lin Shu Zhang Menglei Wang Anqi Wang Puyu Lian 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第1期54-63,共10页
To curb the worsening tropospheric ozone(O3)pollution problem in China,a rapid and accurate identification of O3-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O3 po... To curb the worsening tropospheric ozone(O3)pollution problem in China,a rapid and accurate identification of O3-precursor sensitivity(OPS)is a crucial prerequisite for formulating effective contingency O3 pollution control strategies.However,currently widely-used methods,such as statistical models and numerical models,exhibit inherent limitations in identifying OPS in a timely and accurate manner.In this study,we developed a novel approach to identify OPS based on eXtreme Gradient Boosting model,Shapley additive explanation(SHAP)al-gorithm,and volatile organic compound(VOC)photochemical decay adjustment,using the meteorology and speciated pollutant monitoring data as the input.By comparing the difference in SHAP values between base sce-nario and precursor reduction scenario for nitrogen oxides(NOx)and VOCs,OPS was divided into NOx-limited,VOCs-limited and transition regime.Using the long-lasting O3 pollution episode in the autumn of 2022 at the Guangdong-Hong Kong-Macao Greater Bay Area(GBA)as an example,we demonstrated large spatiotemporal heterogeneities of OPS over the GBA,which were generally shifted from NOx-limited to VOCs-limited from September to October and more inclined to be VOCs-limited at the central and NOx-limited in the peripheral areas.This study developed an innovative OPS identification method by comparing the difference in SHAP value before and after precursor emission reduction.Our method enables the accurate identification of OPS in the time scale of seconds,thereby providing a state-of-the-art tool for the rapid guidance of spatial-specific O3 control strategies. 展开更多
关键词 O3-precursor sensitivity Machine learning Extreme gradient boosting model Shapley algorithm Greater bay area
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