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
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
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
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
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 R<sup>2</sup> 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
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
It is important for regional water resources management to know the agricultural water consumption information several months in advance.Forecasting reference evapotranspiration(ET_(0))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(ET_(0))in the next few months is important for irrigation and reservoir management.Studies on forecasting of multiple-month ahead ET_(0) 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 ET_(0)(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 ET_(0),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 ET_(0) forecasting in summer but use the GWO-XGB model in other seasons.展开更多
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
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 R^(2)(R^(2)=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
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
Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO_(2))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(CO_(2))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(R^(2)),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 R^(2) 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
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
Accurate crown control is paramount for ensuring the quality of hot-rolled strip products.Given the multitude of influencing parameters and the intricate coupling and genetic relationships among them,the conventional ...Accurate crown control is paramount for ensuring the quality of hot-rolled strip products.Given the multitude of influencing parameters and the intricate coupling and genetic relationships among them,the conventional crown control method is no longer sufficient to meet the precision requirements of schedule-free rolling.To address this limitation,an optimization framework for hot-rolled strip crown control was developed based on model-driven digital twin(MDDT).This framework enhances the strip crown control precision by facilitating collaborative operations among physical entities,virtual models,and functional application layers.In virtual modeling,a data-driven approach that integrates the extreme gradient boosting and the improved Harris hawk optimization algorithm was firstly proposed to fit the relationship between key process parameters and strip crown,and a global-local collaborative training strategy was proposed to enhance the model adaptability to diverse working conditions.Subsequently,the influence of crucial process factors on the virtual model was examined through model responses.Furthermore,a novel optimization mode for crown control based on MDDT was established by aligning and reconstructing both the physical and virtual models,thereby enhancing the crown control precision.Finally,data trials were conducted to validate the effectiveness of the proposed framework.The results indicated that the proposed method exhibited satisfactory performance and could be effectively utilized to improve the crown control precision.展开更多
The study by Huang et al,published in the World Journal of Gastroenterology,advances intrahepatic cholangiocarcinoma(ICC)management by developing a machine-learning model to predict textbook outcomes(TO)based on preop...The study by Huang et al,published in the World Journal of Gastroenterology,advances intrahepatic cholangiocarcinoma(ICC)management by developing a machine-learning model to predict textbook outcomes(TO)based on preoperative factors.By analyzing data from 376 patients across four Chinese medical centers,the researchers identified key variables influencing TO,including Child-Pugh classification,Eastern Cooperative Oncology Group score,hepatitis B status,and tumor size.The model,created using logistic regression and the extreme gradient boosting algorithm,demonstrated high predictive accuracy,with area under the curve values of 0.8825 for internal validation and 0.8346 for external validation.The integration of the Shapley additive explanation technique enhances the interpretability of the model,which is crucial for clinical decision-making.This research highlights the potential of machine learning to improve surgical planning and patient outcomes in ICC,opening possibilities for personalized treatment approaches based on individual patient characteristics and risk factors.展开更多
BACKGROUND Severe dengue children with critical complications have been attributed to high mortality rates,varying from approximately 1%to over 20%.To date,there is a lack of data on machine-learning-based algorithms ...BACKGROUND Severe dengue children with critical complications have been attributed to high mortality rates,varying from approximately 1%to over 20%.To date,there is a lack of data on machine-learning-based algorithms for predicting the risk of inhospital mortality in children with dengue shock syndrome(DSS).AIM To develop machine-learning models to estimate the risk of death in hospitalized children with DSS.METHODS This single-center retrospective study was conducted at tertiary Children’s Hospital No.2 in Viet Nam,between 2013 and 2022.The primary outcome was the in-hospital mortality rate in children with DSS admitted to the pediatric intensive care unit(PICU).Nine significant features were predetermined for further analysis using machine learning models.An oversampling method was used to enhance the model performance.Supervised models,including logistic regression,Naïve Bayes,Random Forest(RF),K-nearest neighbors,Decision Tree and Extreme Gradient Boosting(XGBoost),were employed to develop predictive models.The Shapley Additive Explanation was used to determine the degree of contribution of the features.RESULTS In total,1278 PICU-admitted children with complete data were included in the analysis.The median patient age was 8.1 years(interquartile range:5.4-10.7).Thirty-nine patients(3%)died.The RF and XGboost models demonstrated the highest performance.The Shapley Addictive Explanations model revealed that the most important predictive features included younger age,female patients,presence of underlying diseases,severe transaminitis,severe bleeding,low platelet counts requiring platelet transfusion,elevated levels of international normalized ratio,blood lactate and serum creatinine,large volume of resuscitation fluid and a high vasoactive inotropic score(>30).CONCLUSION We developed robust machine learning-based models to estimate the risk of death in hospitalized children with DSS.The study findings are applicable to the design of management schemes to enhance survival outcomes of patients with DSS.展开更多
BACKGROUND The global prevalence of non-alcoholic steatohepatitis(NASH)and its associated risk of adverse outcomes,particularly in patients with advanced liver fibrosis,underscores the importance of early and accurate...BACKGROUND The global prevalence of non-alcoholic steatohepatitis(NASH)and its associated risk of adverse outcomes,particularly in patients with advanced liver fibrosis,underscores the importance of early and accurate diagnosis.AIM To develop a machine learning-based diagnostic model for advanced liver fibrosis in NASH patients.METHODS A total of 749 patients who underwent liver biopsy at Beijing Ditan Hospital,Capital Medical University,between January 2010 and January 2020 were included.Patients were randomly divided into training(n=522)and validation(n=224)cohorts.Five machine learning models were applied to predict advanced liver fibrosis,with feature selection based on Shapley Additive Explanations(SHAP).The diagnostic performance of these models was compared to traditional scores such as the aspartate aminotransferase to platelet ratio index(APRI)and fibrosis index based on the 4 factors(FIB-4),using metrics including the area under the receiver operating characteristic curve(AUROC),decision curve analysis(DCA),and calibration curves.RESULTS The Extreme Gradient Boosting(XGBoost)model outperformed all other machine learning models,achieving an AUROC of 0.934(95%CI:0.914-0.955)in the training cohort and 0.917(95%CI:0.880-0.953)in the validation cohort(P<0.001).Incorporating liver stiffness measurement into the model further improved its performance,with an AUROC of 0.977(95%CI:0.966-0.980)in the training cohort and 0.970(95%CI:0.950-0.990)in the validation cohort,significantly surpassing APRI and FIB-4 scores(P<0.001).The XGBoost model also demonstrated superior clinical utility,as evidenced by DCA and calibration curve analysis in both cohorts.CONCLUSION The XGBoost model provides a highly accurate,non-invasive diagnosis of advanced liver fibrosis in NASH patients,outperforming traditional methods.An online tool based on this model has been developed to assist clinicians in evaluating the risk of advanced liver fibrosis.展开更多
The quasi-rectangular tunnel represents a novel cross-section design,intended to supersede the traditional circular and rectangular tunnel formats.Due to the limited capacity of the tunnel vault to withstand vertical ...The quasi-rectangular tunnel represents a novel cross-section design,intended to supersede the traditional circular and rectangular tunnel formats.Due to the limited capacity of the tunnel vault to withstand vertical loads,an interior column is often installed at the center to enhance its load-bearing capacity.This study aims to develop a hyperstatic reaction method(HRM)for the analysis of deformation and structural integrity in this specific tunnel type.The computational model is validated through comparison with the corresponding finite element method(FEM)analysis.Following comprehensive validation,an ensemble machine learning(ML)model is proposed,using numerical benchmark data,to facilitate real-time design and optimization.Subsequently,three widely used ensemble models,i.e.random forest(RF),gradient boosting decision tree(GBDT),and extreme gradient boosting(XGBoost)are compared to identify the most efficient ML model for replacing the HRM model in the design optimization process.The performance metrics,such as the coefficient of determination R2 of about 0.999 and the mean absolute percentage error(MAPE)of about 1%,indicate that XGBoost outperforms the others,exhibiting excellent agreement with the HRM analysis.Additionally,the model demonstrates high computational efficiency,with prediction times measured in seconds.Finally,the HRM-XGBoost model is integrated with the well-known particle swarm optimization(PSO)for the real-time design optimization of quasi-rectangular tunnels,both with and without the interior column.A feature importance assessment is conducted to evaluate the sensitivity of design input features,enabling the selection of the most critical features for the optimization task.展开更多
Very few studies have benefited from the synergetic implementation of visible,near-infrared,and shortwave infrared(VNIR-SWIR)spectra and terrain attributes in predicting Pb content in agricultural soils.To fill this g...Very few studies have benefited from the synergetic implementation of visible,near-infrared,and shortwave infrared(VNIR-SWIR)spectra and terrain attributes in predicting Pb content in agricultural soils.To fill this gap,this study aimed to predict lead(Pb)contents in agricultural soils by combining machine learning algorithms(MLAs)with VNIR-SWIR spectra or/and terrain attributes under three distinct approaches.Six MLAs were tested,including artificial neural network(ANN),partial least squares regression,support vector machine(SVM),Gaussian process regression(GPR),extreme gradient boosting(EGB),and Cubist.The VNIR-SWIR spectral data were preprocessed by methods of discrete wavelet transformation,logarithmic transformation-Savitzky Golay smoothing,standard normal variate(SNV),multiplicative scatter correction,first derivative(Fi D),and second derivative.In approach 1,MLAs were combined with the preprocessed VNIR-SWIR spectral data.The Cubist-Fi D combination was the most effective,achieving a coefficient of determination(R2)of 0.63,a concordance correlation coefficient(CCC)of 0.51,a mean absolute error(MAE)of 6.87 mg kg^(-1),and a root mean square error(RMSE)of8.66 mg kg^(-1).In approach 2,MLAs were combined with both preprocessed VNIR-SWIR spectral data and terrain attributes,and the EGB-SNV combination yielded superior results with R2of 0.75,CCC of 0.65,MAE of 5.48 mg kg^(-1),and RMSE of 7.34 mg kg^(-1).Approach 3 combined MLAs and terrain attributes,and Cubist demonstrated the best prediction results,with R^(2) of 0.75,CCC of 0.66,MAE of 6.18 mg kg^(-1),and RMSE of 7.71 mg kg^(-1).The cumulative assessment identified the fusion of terrain properties,SNV-preprocessed VNIR-SWIR spectra,and EGB as the optimal method for estimating Pb content in agricultural soils,yielding the highest R2value and minimal error.Comparatively,GPR,ANN,and SVM techniques achieved higher R2values in approaches 2 and 3 but also exhibited higher estimation errors.In conclusion,the study underscores the significance of using relevant auxiliary datasets and appropriate MLAs for accurate Pb content prediction with minimal error in agricultural soils.The findings contribute valuable insights for developing successful soil management strategies based on predictive modeling.展开更多
Accurately revealing the spatial heterogeneity in the trade-offs and synergies of land use functions(LUFs)and their driving factors is imperative for advancing sustainable land utilization and optimizing land use plan...Accurately revealing the spatial heterogeneity in the trade-offs and synergies of land use functions(LUFs)and their driving factors is imperative for advancing sustainable land utilization and optimizing land use planning.This is especially critical for ecologically vulnerable inland river basins in arid regions.However,existing methods struggle to effectively capture complex nonlinear interactions among environmental factors and their multifaceted relationships with trade-offs and synergies of LUFs,especially for the inland river basins in arid regions.Consequently,this study focused on the middle reaches of the Heihe River Basin(MHRB),an arid inland river basin in northwestern China.Using land use,socioeconomic,meteorological,and hydrological data from 2000 to 2020,we analyzed the spatiotemporal patterns of LUFs and their trade-off and synergy relationships from the perspective of production,living,ecological functions.Additionally,we employed an integrated Extreme Gradient Boosting(XGBoost)-SHapley Additive exPlanations(SHAP)framework to investigate the environmental factors influencing the spatial heterogeneity in the trade-offs and synergies of LUFs.Our findings reveal that from 2000 to 2020,the production,living,and ecological functions of land use within the MHRB exhibited an increasing trend,demonstrating a distinct spatial pattern of''high in the southwest and low in the northeast''.Significant spatial heterogeneity defined the trade-off and synergistic relationships,with trade-offs dominating human activity-intensive oasis areas,while synergies prevailed in other areas.During the study period,synergistic relationships between production and living functions and between production and ecological functions were relatively robust,whereas synergies in living-ecological functions remained weaker.Natural factors(digital elevation model(DEM),annual mean temperature,Normalized Difference Vegetation Index(NDVI),and annual precipitation)emerged as the primary factors driving the trade-offs and synergies of LUFs,followed by socioeconomic factors(population density,Gross Domestic Product(GDP),and land use intensity),while distance factors(distance to water bodies,distance to residential areas,and distance to roads)exerted minimal influence.Notably,the interactions among NDVI,annual mean temperature,DEM,and land use intensity exerted the most substantial impacts on the relationships among LUFs.This study provides novel perspectives and methodologies for unraveling the mechanisms underlying the spatial heterogeneity in the trade-offs and synergies of LUFs,offering scientific insights to inform regional land use planning and sustainable natural resource management in inland river basins in arid regions.展开更多
基金Supported by the Agency Natural Science Foundation of Fujian Province,China,No.2022J011285 and No.2023J011480.
文摘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.
文摘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.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00222536).
文摘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.
基金The National Natural Science Foundation of China(No.52361165658,52378318,52078459).
文摘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.
文摘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 R<sup>2</sup> 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.
基金financial support from High-end Foreign Expert Introduction program(No.G20190022002)Chongqing Construction Science and Technology Plan Project(2019-0045)as well as Chongqing Engineering Research Center of Disaster Prevention&Control for Banks and Structures in Three Gorges Reservoir Area(Nos.SXAPGC18ZD01 and SXAPGC18YB03)。
文摘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.
基金This study was jointly supported by the National Natural Science Foundation of China(Nos.51879196,51790533,51709143)Jiangxi Natural Science Foundation of China(No.20181BAB206045).
文摘It is important for regional water resources management to know the agricultural water consumption information several months in advance.Forecasting reference evapotranspiration(ET_(0))in the next few months is important for irrigation and reservoir management.Studies on forecasting of multiple-month ahead ET_(0) 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 ET_(0)(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 ET_(0),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 ET_(0) forecasting in summer but use the GWO-XGB model in other seasons.
文摘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.
文摘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 R^(2)(R^(2)=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.
基金The Second Century Fund(C2F),Chulalongkorn University.
文摘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.
基金funding provided by the China Scholarship Council (Nos.202008440524 and 202006370006)supported by the Distinguished Youth Science Foundation of Hunan Province of China (No.2022JJ10073)+1 种基金Innovation Driven Project of Central South University (No.2020CX040)Shenzhen Sciencee and Technology Plan (No.JCYJ20190808123013260).
文摘Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO_(2))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(R^(2)),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 R^(2) 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.
基金supported by the“Microsoft Grant Award:AI for Health COVID-19″The RISC-19-ICU reg-istry is supported by the Swiss Society of Intensive Care Medicine and funded by internal resources of the Institute of Intensive Care Medicine,of the University Hospital Zurich and by unrestricted grants from CytoSorbents Europe GmbH(Berlin,Germany)+1 种基金Union Bancaire Privée(Zurich,Switzerland)The sponsors had no role in the design of the study,the collection and analysis of the data,or the preparation of the manuscript.
文摘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.
基金financially supported by the National Key Research and Development Program of China(Grant No.2023YFB3710204)Guangxi Science and Technology Major Program(Grant No.AA23023028-1)+1 种基金Natural Science Foundation of Heilongjiang Province of China for Distinguished Young Scientists(Grant No.JQ2022E007)Xinjiang Production and Construction Corps Science and Technology Plan(Grant No.2023AA003).
文摘Accurate crown control is paramount for ensuring the quality of hot-rolled strip products.Given the multitude of influencing parameters and the intricate coupling and genetic relationships among them,the conventional crown control method is no longer sufficient to meet the precision requirements of schedule-free rolling.To address this limitation,an optimization framework for hot-rolled strip crown control was developed based on model-driven digital twin(MDDT).This framework enhances the strip crown control precision by facilitating collaborative operations among physical entities,virtual models,and functional application layers.In virtual modeling,a data-driven approach that integrates the extreme gradient boosting and the improved Harris hawk optimization algorithm was firstly proposed to fit the relationship between key process parameters and strip crown,and a global-local collaborative training strategy was proposed to enhance the model adaptability to diverse working conditions.Subsequently,the influence of crucial process factors on the virtual model was examined through model responses.Furthermore,a novel optimization mode for crown control based on MDDT was established by aligning and reconstructing both the physical and virtual models,thereby enhancing the crown control precision.Finally,data trials were conducted to validate the effectiveness of the proposed framework.The results indicated that the proposed method exhibited satisfactory performance and could be effectively utilized to improve the crown control precision.
文摘The study by Huang et al,published in the World Journal of Gastroenterology,advances intrahepatic cholangiocarcinoma(ICC)management by developing a machine-learning model to predict textbook outcomes(TO)based on preoperative factors.By analyzing data from 376 patients across four Chinese medical centers,the researchers identified key variables influencing TO,including Child-Pugh classification,Eastern Cooperative Oncology Group score,hepatitis B status,and tumor size.The model,created using logistic regression and the extreme gradient boosting algorithm,demonstrated high predictive accuracy,with area under the curve values of 0.8825 for internal validation and 0.8346 for external validation.The integration of the Shapley additive explanation technique enhances the interpretability of the model,which is crucial for clinical decision-making.This research highlights the potential of machine learning to improve surgical planning and patient outcomes in ICC,opening possibilities for personalized treatment approaches based on individual patient characteristics and risk factors.
文摘BACKGROUND Severe dengue children with critical complications have been attributed to high mortality rates,varying from approximately 1%to over 20%.To date,there is a lack of data on machine-learning-based algorithms for predicting the risk of inhospital mortality in children with dengue shock syndrome(DSS).AIM To develop machine-learning models to estimate the risk of death in hospitalized children with DSS.METHODS This single-center retrospective study was conducted at tertiary Children’s Hospital No.2 in Viet Nam,between 2013 and 2022.The primary outcome was the in-hospital mortality rate in children with DSS admitted to the pediatric intensive care unit(PICU).Nine significant features were predetermined for further analysis using machine learning models.An oversampling method was used to enhance the model performance.Supervised models,including logistic regression,Naïve Bayes,Random Forest(RF),K-nearest neighbors,Decision Tree and Extreme Gradient Boosting(XGBoost),were employed to develop predictive models.The Shapley Additive Explanation was used to determine the degree of contribution of the features.RESULTS In total,1278 PICU-admitted children with complete data were included in the analysis.The median patient age was 8.1 years(interquartile range:5.4-10.7).Thirty-nine patients(3%)died.The RF and XGboost models demonstrated the highest performance.The Shapley Addictive Explanations model revealed that the most important predictive features included younger age,female patients,presence of underlying diseases,severe transaminitis,severe bleeding,low platelet counts requiring platelet transfusion,elevated levels of international normalized ratio,blood lactate and serum creatinine,large volume of resuscitation fluid and a high vasoactive inotropic score(>30).CONCLUSION We developed robust machine learning-based models to estimate the risk of death in hospitalized children with DSS.The study findings are applicable to the design of management schemes to enhance survival outcomes of patients with DSS.
基金Supported by the Natural Science Foundation of China,No.81970512the Beijing Hospitals Authority Youth Programme,No.QMl220201802+1 种基金the Beijing Traditional Chinese Medicine Science and Technology Development Fund Project,No.Qn-2020-25High-Level Public Health Technical Personnel Construction Project.
文摘BACKGROUND The global prevalence of non-alcoholic steatohepatitis(NASH)and its associated risk of adverse outcomes,particularly in patients with advanced liver fibrosis,underscores the importance of early and accurate diagnosis.AIM To develop a machine learning-based diagnostic model for advanced liver fibrosis in NASH patients.METHODS A total of 749 patients who underwent liver biopsy at Beijing Ditan Hospital,Capital Medical University,between January 2010 and January 2020 were included.Patients were randomly divided into training(n=522)and validation(n=224)cohorts.Five machine learning models were applied to predict advanced liver fibrosis,with feature selection based on Shapley Additive Explanations(SHAP).The diagnostic performance of these models was compared to traditional scores such as the aspartate aminotransferase to platelet ratio index(APRI)and fibrosis index based on the 4 factors(FIB-4),using metrics including the area under the receiver operating characteristic curve(AUROC),decision curve analysis(DCA),and calibration curves.RESULTS The Extreme Gradient Boosting(XGBoost)model outperformed all other machine learning models,achieving an AUROC of 0.934(95%CI:0.914-0.955)in the training cohort and 0.917(95%CI:0.880-0.953)in the validation cohort(P<0.001).Incorporating liver stiffness measurement into the model further improved its performance,with an AUROC of 0.977(95%CI:0.966-0.980)in the training cohort and 0.970(95%CI:0.950-0.990)in the validation cohort,significantly surpassing APRI and FIB-4 scores(P<0.001).The XGBoost model also demonstrated superior clinical utility,as evidenced by DCA and calibration curve analysis in both cohorts.CONCLUSION The XGBoost model provides a highly accurate,non-invasive diagnosis of advanced liver fibrosis in NASH patients,outperforming traditional methods.An online tool based on this model has been developed to assist clinicians in evaluating the risk of advanced liver fibrosis.
基金funded by the Hanoi University of Mining and Geology(Grant No.T23-44)The research is also funded by the German Research Foundation(DFG e Project number 518862444)in collaboration with the Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number DFG.105e2022.03The third author was funded by the Postdoctoral Scholarship Program of the Vingroup Innovation Foundation(VINIF)(VINIF.2023.STS.15).
文摘The quasi-rectangular tunnel represents a novel cross-section design,intended to supersede the traditional circular and rectangular tunnel formats.Due to the limited capacity of the tunnel vault to withstand vertical loads,an interior column is often installed at the center to enhance its load-bearing capacity.This study aims to develop a hyperstatic reaction method(HRM)for the analysis of deformation and structural integrity in this specific tunnel type.The computational model is validated through comparison with the corresponding finite element method(FEM)analysis.Following comprehensive validation,an ensemble machine learning(ML)model is proposed,using numerical benchmark data,to facilitate real-time design and optimization.Subsequently,three widely used ensemble models,i.e.random forest(RF),gradient boosting decision tree(GBDT),and extreme gradient boosting(XGBoost)are compared to identify the most efficient ML model for replacing the HRM model in the design optimization process.The performance metrics,such as the coefficient of determination R2 of about 0.999 and the mean absolute percentage error(MAPE)of about 1%,indicate that XGBoost outperforms the others,exhibiting excellent agreement with the HRM analysis.Additionally,the model demonstrates high computational efficiency,with prediction times measured in seconds.Finally,the HRM-XGBoost model is integrated with the well-known particle swarm optimization(PSO)for the real-time design optimization of quasi-rectangular tunnels,both with and without the interior column.A feature importance assessment is conducted to evaluate the sensitivity of design input features,enabling the selection of the most critical features for the optimization task.
基金supported by an institutional Ph.D.grant(No.21130/1312/3131)from the Faculty of Agrobiology,Food,and Natural Resources at the Czech University of Life Sciences Prague(CZU),Czech Republic。
文摘Very few studies have benefited from the synergetic implementation of visible,near-infrared,and shortwave infrared(VNIR-SWIR)spectra and terrain attributes in predicting Pb content in agricultural soils.To fill this gap,this study aimed to predict lead(Pb)contents in agricultural soils by combining machine learning algorithms(MLAs)with VNIR-SWIR spectra or/and terrain attributes under three distinct approaches.Six MLAs were tested,including artificial neural network(ANN),partial least squares regression,support vector machine(SVM),Gaussian process regression(GPR),extreme gradient boosting(EGB),and Cubist.The VNIR-SWIR spectral data were preprocessed by methods of discrete wavelet transformation,logarithmic transformation-Savitzky Golay smoothing,standard normal variate(SNV),multiplicative scatter correction,first derivative(Fi D),and second derivative.In approach 1,MLAs were combined with the preprocessed VNIR-SWIR spectral data.The Cubist-Fi D combination was the most effective,achieving a coefficient of determination(R2)of 0.63,a concordance correlation coefficient(CCC)of 0.51,a mean absolute error(MAE)of 6.87 mg kg^(-1),and a root mean square error(RMSE)of8.66 mg kg^(-1).In approach 2,MLAs were combined with both preprocessed VNIR-SWIR spectral data and terrain attributes,and the EGB-SNV combination yielded superior results with R2of 0.75,CCC of 0.65,MAE of 5.48 mg kg^(-1),and RMSE of 7.34 mg kg^(-1).Approach 3 combined MLAs and terrain attributes,and Cubist demonstrated the best prediction results,with R^(2) of 0.75,CCC of 0.66,MAE of 6.18 mg kg^(-1),and RMSE of 7.71 mg kg^(-1).The cumulative assessment identified the fusion of terrain properties,SNV-preprocessed VNIR-SWIR spectra,and EGB as the optimal method for estimating Pb content in agricultural soils,yielding the highest R2value and minimal error.Comparatively,GPR,ANN,and SVM techniques achieved higher R2values in approaches 2 and 3 but also exhibited higher estimation errors.In conclusion,the study underscores the significance of using relevant auxiliary datasets and appropriate MLAs for accurate Pb content prediction with minimal error in agricultural soils.The findings contribute valuable insights for developing successful soil management strategies based on predictive modeling.
基金funded by the University Teachers Innovation Fund Project of Gansu Province(2025A-001)the Northwest Normal University Young Teachers'Scientific Research Ability Improvement Plan(NWNULKQN2024-20).
文摘Accurately revealing the spatial heterogeneity in the trade-offs and synergies of land use functions(LUFs)and their driving factors is imperative for advancing sustainable land utilization and optimizing land use planning.This is especially critical for ecologically vulnerable inland river basins in arid regions.However,existing methods struggle to effectively capture complex nonlinear interactions among environmental factors and their multifaceted relationships with trade-offs and synergies of LUFs,especially for the inland river basins in arid regions.Consequently,this study focused on the middle reaches of the Heihe River Basin(MHRB),an arid inland river basin in northwestern China.Using land use,socioeconomic,meteorological,and hydrological data from 2000 to 2020,we analyzed the spatiotemporal patterns of LUFs and their trade-off and synergy relationships from the perspective of production,living,ecological functions.Additionally,we employed an integrated Extreme Gradient Boosting(XGBoost)-SHapley Additive exPlanations(SHAP)framework to investigate the environmental factors influencing the spatial heterogeneity in the trade-offs and synergies of LUFs.Our findings reveal that from 2000 to 2020,the production,living,and ecological functions of land use within the MHRB exhibited an increasing trend,demonstrating a distinct spatial pattern of''high in the southwest and low in the northeast''.Significant spatial heterogeneity defined the trade-off and synergistic relationships,with trade-offs dominating human activity-intensive oasis areas,while synergies prevailed in other areas.During the study period,synergistic relationships between production and living functions and between production and ecological functions were relatively robust,whereas synergies in living-ecological functions remained weaker.Natural factors(digital elevation model(DEM),annual mean temperature,Normalized Difference Vegetation Index(NDVI),and annual precipitation)emerged as the primary factors driving the trade-offs and synergies of LUFs,followed by socioeconomic factors(population density,Gross Domestic Product(GDP),and land use intensity),while distance factors(distance to water bodies,distance to residential areas,and distance to roads)exerted minimal influence.Notably,the interactions among NDVI,annual mean temperature,DEM,and land use intensity exerted the most substantial impacts on the relationships among LUFs.This study provides novel perspectives and methodologies for unraveling the mechanisms underlying the spatial heterogeneity in the trade-offs and synergies of LUFs,offering scientific insights to inform regional land use planning and sustainable natural resource management in inland river basins in arid regions.
