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A Scale Separation Hybrid Predictive Model and Its Application to Predict Summer Monthly Precipitation in Northeast China 认领 引用
1
作者 Lei YU Aihui WANG Changzheng LIU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第3期504-528,共25页
Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying clima... Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90. 展开更多
关键词 Northeast China precipitation scale separation approach statistical predictive model recurrent neural network predictive model
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Development and validation of machine learningbased in-hospital mortality predictive models for acute aortic syndrome in emergency departments 认领 引用
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作者 Yuanwei Fu Yilan Yang +6 位作者 Hua Zhang Daidai Wang Qiangrong Zhai Lanfang Du Nijiati Muyesai YanxiaGao Qingbian Ma 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2026年第1期43-49,共7页
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita... BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation. 展开更多
关键词 Emergency department Acute aortic syndrome Mortality Predictive model Machine learning Algorithms
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Predictive modeling for mechanical properties of cold-rolled strip steel based on random forest regression and whale optimization algorithm 认领 引用
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作者 Hong-Lei Cai Yi-Ming Fang +3 位作者 Le Liu Li-Hui Ren Zhen-Dong Liu Xiao-Dong Zhao 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第3期73-87,共15页
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n... In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R2)and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability. 展开更多
关键词 Cold-rolled strip steel Mechanical property Predictive modeling Random forest regression Whale optimization algorithm
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Constructing a predictive model for postpartum anxiety in patients with preeclampsia based on multidimensional indicators and its application 认领 引用 被引量：1
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作者 Xiao-Yan Zhang Yun Shi +3 位作者 Yi-Ting Lu Ya-Jun Zhong Jia-Xian Wu Xiao-Qing Wang 《World Journal of Psychiatry》 SCIE 2026年第3期264-273,共10页
BACKGROUND Preeclampsia(PE)substantially increases the risk of postpartum anxiety,yet limited research has examined how disease onset and clinical features,such as blood pressure control and body mass index(BMI)change... BACKGROUND Preeclampsia(PE)substantially increases the risk of postpartum anxiety,yet limited research has examined how disease onset and clinical features,such as blood pressure control and body mass index(BMI)changes during pregnancy,affect this risk.AIM To develop and apply a predictive model for postpartum anxiety disorder in patients with PE based on multidimensional indicators.METHODS A cross-sectional study was conducted among 196 patients with PE admitted to the Department of Obstetrics,Ninth People’s Hospital of Suzhou(Affiliated with Soochow University),from June 2019 to June 2024.According to the self-rating anxiety scale at six weeks postpartum,participants were divided into anxiety and no-anxiety groups.Two data sets were analyzed,and multivariate logistic regression was performed to identify risk and protective factors.Regression coefficients and constants were used to construct the predictive model.Model performance was evaluated using the receiver operating characteristic curve and area under the curve,along with a goodness-of-fit test.The model was then validated with clinical data.RESULTS Of the 196 patients with PE evaluated using the self-rating anxiety scale at six weeks postpartum,51(26.02%)patients showed anxiety symptoms.Significant group differences(P<0.05)were observed for blood pressure control,BMI increase,hematocrit(Hct),family relationships,and psychological resilience.Logistic regression indicated that,poor blood pressure control,greater BMI increase,elevated Hct levels,and strained family relationships during pregnancy were risk factors for postpartum anxiety in patients with PE(P<0.05),whereas higher psychological resilience was a protective factor(P<0.05).The prediction model was defined as:Logit(P)=0.684×pregnancy blood pressure control+0.805×pregnancy BMI increase+0.756×Hct+1.063×family relationship-1.105×psychological resilience score-5.487.The model’s area under the curve(0.908)exceeded that of individual indicators:Blood pressure control(0.794),BMI increase(0.814),Hct(0.808),family relationships(0.840),and psychological resilience(0.833).The goodness-of-fit test showed no overfitting(χ2=1.904,P=0.725).Clinical validation demonstrated sensitivity of 85.71%,specificity of 87.72%,and accuracy of 87.18%.CONCLUSION Postpartum anxiety risk in patients with PE is associated with poor blood pressure control,excessive BMI gain,elevated Hct index,and poor family relationships,while strong psychological resilience serve as a protective factor.The developed prediction model effectively supports clinical assessment and targeted management of postpartum anxiety in patients with PE. 展开更多
关键词 Preeclampsia Postpartum Anxiety disorder Influencing factors Prediction model
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MICP-induced reduction of bound water film thickness in granitic residual soil:Development and AFM validation of a soil mechanics-based predictive model 认领 引用
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作者 Chenyu Zhu Yanning Wang Jingrui Peng 《Biogeotechnics》 2026年第3期58-67,共10页
The mechanical properties of clayey soils are intimately linked to Bound Water Content(BWC).Microbially Induced Calcium Precipitation(MICP),as an emerging technology for slope treatment and foundation engineering,has ... The mechanical properties of clayey soils are intimately linked to Bound Water Content(BWC).Microbially Induced Calcium Precipitation(MICP),as an emerging technology for slope treatment and foundation engineering,has an unclear mechanism in the influence on BWC of soil.Therefore,quantitative analysis of the Thickness of the Bound Water Film(TBWF)—a direct microscale characterization of BWC—holds significant importance.To quantitatively analyze the influence of MICP technology on TBWF,this study proposes a TBWF prediction model based on soil mechanics theory and validates effectiveness through Atomic Force Microscopy(AFM)experiments.Taking Granite Residual Soil(GRS)as the research object,the study revealed that MICP technology significantly reduces TBWF:when the cementation solution concentration was 1.0 mol/L,TBWF decreased from 48.297 nm(untreated control)to 34.561 nm(1.0 mol/L treatment group),a reduction of 28.44%.Further investigations revealed that MICP treatment lowers the liquid limit moisture content of soil while increasing specific gravity,bound water density,and specific surface area.However,when the concentration exceeded 1.0 mol/L,TBWF rebounded due to suppressed urease activity.AFM experimental data showed high consistency with theoretical model predictions,verifying the model’s reliability.This study provides microscopic mechanism support for the application of MICP technology in geotechnical engineering fields such as landslide prevention and slope reinforcement,establishes a new method for quantitative analysis of the bound water film,and holds important significance for improving the effectiveness of geological disaster prevention and control. 展开更多
关键词 Bound water film Clay MICP AFM Predictive modeling
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Construction and validation of a predictive model for the risk of emergence delirium in older adult patients 认领 引用
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作者 Yi Xin Bin He +7 位作者 Xiao-Hui Wei Ya-Ling Yan Chen Huang Chun-Yan Gao Shuo Wang Guang-Ming Zhang Rui Li Ying Wu 《World Journal of Psychiatry》 SCIE 2026年第6期389-405,共17页
BACKGROUND Emergence delirium(ED)is a common postoperative complication in older adult patients,posing a significant burden on both patients and medical staff.Despite its prevalence,there is a notable lack of research... BACKGROUND Emergence delirium(ED)is a common postoperative complication in older adult patients,posing a significant burden on both patients and medical staff.Despite its prevalence,there is a notable lack of research focused on identifying predictive factors and constructing models for ED in the post-anesthesia care unit.Therefore,developing a risk prediction model for ED in older adult patients is imperative.We anticipate that such a model would demonstrate strong predictive efficacy and be applicable in clinical settings.AIM To develop and validate an ED risk-prediction model for early intervention in older adults.METHODS This study enrolled 705 older surgical patients(January 2024 to October 2024)for modeling and 115(November 2024 to December 2024)for validation.Using least absolute shrinkage and selection operator and multivariable logistic regression,we developed a predictive model with an online dynamic nomogram.Internal(10-fold crossvalidation)and external validation demonstrated strong discrimination,calibration,and clinical utility.RESULTS The incidence of ED in older adult patients postoperatively was found to be 17.16%.Independent risk factors for postoperative ED included preoperative Mini-Mental State Examination score,preoperative albumin level,surgical duration,surgical risk score,number of indwelling catheters,and extubation time(all P<0.05).The model demonstrated the area under curve(AUC)of 0.924[95%confidence interval(CI):0.897-0.951],with the calibration curve closely aligning with the ideal curve.The Hosmer-Lemeshow test yieldedχ2=7.934,P=0.541,indicating good clinical utility.Internal validation resulted in an AUC of 0.920(95%CI:0.571-0.959),while external validation showed an AUC of 0.931(95%CI:0.866-0.997).The calibration curve for the validation cohort closely matched the ideal curve,with the Hosmer-Lemeshow test showingχ2=5.772,P=0.763,further supporting its clinical applicability.CONCLUSION The dynamic nomogram accurately predicts ED risk in older adults,aiding early identification and clinical intervention. 展开更多
关键词 Older adult Emergence delirium Risk factors Prediction model Nomogram
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Application research on the predictive model for violent behavior in hospitalized patients with severe mental disorders 认领 引用
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作者 Ting Wang Lin Wang +4 位作者 Ping Zhao Jiao-Jiao Sun Li-Ni Gao Jia Li Ya-Qin Zhao 《World Journal of Psychiatry》 SCIE 2026年第2期247-258,共12页
BACKGROUND To understand the current situation of violent behavior among hospitalized patients with severe mental disorders(SMDs),analyze its influencing factors,establish a predictive model and draw a nomogram,provid... BACKGROUND To understand the current situation of violent behavior among hospitalized patients with severe mental disorders(SMDs),analyze its influencing factors,establish a predictive model and draw a nomogram,providing screening tools for medical staff to accurately identify SMDs who have violent behavior and the direction of early intervention.AIM To investigate the determinants of violent actions in hospitalized patients with SMDs.METHODS This research included 440 inpatients with SMDs who were admitted to the Wutaishan Hospital from January 2025 to June 2025.Data collection and analysis aimed to pinpoint independent contributors linked to aggression in this patient group.An advanced logistic regression analysis with multiple variables was performed using R,followed by the creation of a line chart to display the forecast outcomes of the model.RESULTS Of 120 patients exhibited violent behavior(incidence rate=27.30%).Education level,cigarette smoking,length of hospitalization,age,psychotic symptoms based on the Brief Psychiatric Rating Scale,and C-reactive protein were independent risk factors for violent behavior.Education level and age served as protective elements among the factors analyzed.The receiver operating characteristic curve area for the training and test sets was calculated to be 0.94 and 0.93,respectively.The calibration graph demonstrated that the model was accurately adjusted.The clinical decision curve demonstrated that the model provided significant practical benefits.CONCLUSION The predictive mode provided a valuable theoretical basis for ward staff to identify inpatients with SMDs at elevated risk of aggression in the early phase. 展开更多
关键词 Severe mental disorders Violent behavior Influencing factors Prediction model Flowchart
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High-precision predictive modeling of AWJM for S275 steel machining 认领 引用
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作者 Fermin Bañon Sergio Martín-Béjar +2 位作者 Carolina Bermudo Francisco J.Trujillo Lorenzo Sevilla 《ENGINEERING Mechanical Engineering》 SCIE CAS CSCD 2026年第1期113-137,共25页
This research enhances the precision and efficacy of abrasive waterjet machining(AWJM)of S275 carbon steel.To this end,a precise predictive framework has been developed using artificial neural networks(ANNs)and respon... This research enhances the precision and efficacy of abrasive waterjet machining(AWJM)of S275 carbon steel.To this end,a precise predictive framework has been developed using artificial neural networks(ANNs)and response surface models(RSMs).By employing an innovative Vectorized Macrographic Analysis,the cutting geometries are accurately mapped and the correlation between width at various depths and energy dissipation is established.The fit accuracy of the ANN is 99%,while that of the RSM is 90%.Furthermore,a minimum cutting energy threshold of 52.20 kJ/m2has been identified,which represents the optimal efficiency threshold.These developments highlight ANN's ability to model complex AWJM interactions,improving machining precision and adaptability. 展开更多
关键词 abrasive waterjet machining S275 carbon steel taper angle optimization response surface methodology artificial neural network comparative predictive model
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Hydrological Extremes under Climate Change:Advances in Predictive Modeling and Risk Assessment 认领 引用
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作者 Lei Gao Min’kuo Cai +2 位作者 Changjiang Cai Fachun She Zhexu Li 《Journal of Environmental & Earth Sciences》 CAS 2026年第2期340-360,共21页
Hydrological extremes,such as floods,droughts,and compound events,are extremely dangerous to human societies,ecosystems,and infrastructures,whose frequency and severity are affected by climate change more and more.Eff... Hydrological extremes,such as floods,droughts,and compound events,are extremely dangerous to human societies,ecosystems,and infrastructures,whose frequency and severity are affected by climate change more and more.Effective disaster preparedness,water resource management,and climate adaptation have to do with accurate prediction and extensive risk assessment.This review sums up recent progress in predictive modeling and risk assessment systems in the framework of hydrological extremes in the changing climatic conditions.Statistical and empirical techniques,including extreme value theory and nonstationary frequency analysis,give probabilistic information using historic records,whereas process-based models give an understanding of physical hydrological processes at different climate and land-use conditions.New information-based and hybrid methods that use machine learning and high-resolution data take advantage of the complexity and nonlinearities and enhance the predictive power.Hazard,exposure,vulnerability,and adaptive capacity risk assessment models allow predictive output to be translated into actionable decision support,with socio-economic aspects and analysis of the scenario.Case studies of various regions across the globe show the use of these techniques to address floods,droughts,and compound events,with success and current problems.The review also addresses current trends such as compound hazard,multi-hazard integration,AI-enabled modelling,and cross-sectoral decision support,and outlines research priorities of improving predictive capability and resilience.This review will inform researchers,policymakers,and practitioners by offering a synthesis of all the effects of the hydrological extremes in climate change to formulate sound strategies for alleviating these effects. 展开更多
关键词 Hydrological Extremes Climate Change Predictive Modeling Risk Assessment Compound Events
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Construction and Use of a Predictive Model for the Risk of ICU-acquired Weakness 认领 引用
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作者 Yaling Li Baoyi Yang Zhenzhen Rao 《Journal of Clinical and Nursing Research》 2026年第4期198-212,共15页
Objective:To explore the risk factors of acquired weakness in ICU patients and construct a prediction model.Methods:Using convenience sampling method,245 intensive care patients admitted to ICUs of three tertiary hosp... Objective:To explore the risk factors of acquired weakness in ICU patients and construct a prediction model.Methods:Using convenience sampling method,245 intensive care patients admitted to ICUs of three tertiary hospitals in Shiyan City from March 2022 to April 2023 were selected,of which 172 cases from March to December 2022 were used as the modeling group and 73 cases from January to April 2023 were used as the validation group.The predictive effect of the model was examined using the area under the curve(AUC)of the receiver operating characteristic curve(ROC)and Hosmer-Lemeshow goodness-of-fit,and the model column line graph was plotted.Results:The incidence of ICU-acquired weakness was 36.63%(63/172)and 38.37%(28/73)in the modeling and validation groups,respectively.Patient’s history of alcohol consumption,mode of admission to the ICU,treatment with CRRT,mechanical ventilation,restraint braking,use of analgesics,use of norepinephrine,use of glucocorticoids,and length of stay in the ICU were the independent influencing factors of acquired weakness in ICU patients(p<0.05).The AUCs of the modeling and validation groups were 0.979 and 0.887,respectively.Conclusion:The risk prediction model constructed in this study can predict the risk of acquired weakness in ICU,which can provide a reference for healthcare professionals to formulate interventions at an early stage. 展开更多
关键词 Intensive care unit Acquired weakness Influence factor analysis Predictive modeling Columnar plots
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A Predictive Model for the Elastic Modulus of High-Strength Concrete Based on Coarse Aggregate Characteristics 认领 引用
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作者 LI Liangshun LI Huajian +2 位作者 HUANG Fali YANG Zhiqiang DONG Haoliang 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2026年第1期121-137,共17页
To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the stre... To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%. 展开更多
关键词 elastic modulus prediction model mineralogical influence mechanism
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Risk factors for inadequate bowel preparation:A retrospective study of 7931 paired colonoscopy cases with a predictive model 认领 引用
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作者 Hai-Bo Xiong Qin-Xing Cao +4 位作者 Wei Deng Hong-Guang Zhou Xian-Ting Lai Qian-Qiu Zhang Jun Li 《World Journal of Gastrointestinal Endoscopy》 2026年第7期44-53,共10页
BACKGROUND Inadequate bowel preparation negatively impacts adenoma detection rates and overall colonoscopy quality;however,the associated risk factors have yet to be fully elucidated in large paired-cohort studies.AIM... BACKGROUND Inadequate bowel preparation negatively impacts adenoma detection rates and overall colonoscopy quality;however,the associated risk factors have yet to be fully elucidated in large paired-cohort studies.AIM To identify independent risk factors associated with inadequate bowel preparation and to develop and validate a clinical prediction model for Chinese patients undergoing colonoscopy.METHODS A total of 7931 patients who underwent two or more colonoscopies were retrospectively enrolled in this study.Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for inadequate bowel preparation.The predictive performance of the model was evaluated using 10-fold cross-validation,bootstrap resampling,temporal validation,and subgroup analyses.RESULTS The overall rate of inadequate bowel preparation was 13.67%.Independent risk factors included older age,male sex,afternoon colonoscopy,diabetes mellitus,constipation,and a history of inadequate bowel preparation(all P<0.05),whereas inpatient status was identified as a protective factor(odds ratio=0.391,P<0.001).The prediction model demonstrated good discriminatory ability[area under the curve(AUC)=0.699]and satisfactory calibration.In addition,the simplified scoring system yielded an AUC of 0.687 and exhibited a clear gradient of increasing risk across score categories.CONCLUSION A history of inadequate bowel preparation is the strongest predictor of subsequent preparation failure.Inpatient status is associated with significantly better bowel preparation quality than outpatient status.The simplified risk scoring system provides a practical tool for identifying patients at high risk of inadequate bowel preparation and facilitating personalized preparation strategies.To our knowledge,this is the first large-scale paired-colonoscopy study to quantify the recurrent nature of bowel preparation failure and to demonstrate the protective effect of inpatient status after adjustment for multiple potential confounders. 展开更多
关键词 Bowel preparation Colonoscopy Risk factors Prediction model Simplified scoring system Inpatient status
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Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection 认领 引用 被引量：10
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作者 Yi-Heng Shi Jun-Liang Liu +5 位作者 Cong-Cong Cheng Wen-Ling Li Han Sun Xi-Liang Zhou Hong Wei Su-Juan Fei 《World Journal of Gastroenterology》 SCIE CAS 2025年第11期46-62,共17页
BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR... BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations. 展开更多
关键词 Colorectal polyps Machine learning Predictive model Risk factors SHapley Additive exPlanation
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Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding 认领 引用 被引量：4
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作者 Shang Gao Haoxiang Wang +2 位作者 Han Huang Zhigang Dong Renke Kang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2025年第3期423-449,共27页
Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and sub... Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials. 展开更多
关键词 surface quality grinding predictive models semiconductor materials surface roughness subsurface damage depth
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Multiparametric magnetic resonance imaging-based predictive model for chemotherapy response in colorectal cancer patients with gene mutations 认领 引用 被引量：3
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作者 Wen-Yan Kang Wen-Ming Deng +4 位作者 Xiao-Qin Ye Yi-Hong Zhong Xiao-Jun Li Ling-Ling Feng De-Hong Luo 《World Journal of Gastrointestinal Oncology》 SCIE 2025年第10期280-289,共10页
BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),w... BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),with its noninvasive capability to assess tumor characteristics in detail,has shown promise in evaluating treatment response and predicting therapeutic outcomes.This technology holds potential for guiding personalized treatment strategies tailored to individual patient profiles,enhancing the precision and effectiveness of colorectal cancer care.AIM To create a multiparametric MRI-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.METHODS This retrospective study was conducted in a tertiary hospital,analyzing 157 colorectal cancer patients with gene mutations treated between August 2022 and December 2023.Based on chemotherapy outcomes,the patients were categorized into favorable(n=60)and unfavorable(n=50)response groups.Univariate and multivariate logistic regression analyses were performed to identify independent predictors of chemotherapy efficacy.A predictive nomogram was constructed using significant variables,and its performance was assessed using the area under the receiver operating characteristic curve(AUC)in both training and validation sets.RESULTS Univariate analysis identified that tumor differentiation,T2 signal intensity ratio,tumor-to-anal margin distance,and MRI-detected lymph node metastasis as significantly associated with chemotherapy response(P<0.05).Multivariate Logistics regression confirmed these four parameters as independent predictors.The predictive model demonstrated strong discrimination,with an AUC of 0.938(sensitivity:86%;specificity:92%)in the training set,and 0.942(sensitivity:100%;specificity:83%)in the validation set.CONCLUSION We established and validated a multiparametric MRI-based model for predicting chemotherapy response in colorectal cancer patients with gene mutations.This model holds promise for guiding individualized treatment strategies. 展开更多
关键词 Colorectal cancer RAS gene mutation Multiparametric magnetic resonance imaging Chemotherapy Predictive model
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Development and validation of a predictive model for testicular atrophy after orchiopexy in children with testicular torsion 认领 引用
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作者 Jia Wei Zixia Li +5 位作者 Yuexin Wei Daxing Tang Guannan Bai Lidong Men Shengde Wu Xiang Yan 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2025年第4期387-391,共5页
Testicular torsion is a urological emergency that requires prompt diagnosis and treatment,accounting for 10%-15%of cases of acute scrotum.[1]It occurs most frequently during the perinatal period and adolescence and ca... Testicular torsion is a urological emergency that requires prompt diagnosis and treatment,accounting for 10%-15%of cases of acute scrotum.[1]It occurs most frequently during the perinatal period and adolescence and can occur at any age.[2]The incidence of testicular torsion is 1/4,000 in males under 25 years of age and 1/160 in males over 25 years of age.[3]Unilateral torsion is relatively common,with a higher incidence on the left side.Testicular torsion is typically managed through surgical exploration.Necrotic testes,identified by a black appearance,require orchiectomy.[4] 展开更多
关键词 surgical explorationnecr urological emergency acute scrotum orchiopexy children testicular atrophy testicular torsion predictive model
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Development and validation of a predictive model for portal-systemic venous invasion grading in borderline resectable pancreatic cancer 认领 引用
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作者 Fang-Fei Wang Xiao-Di Dai +2 位作者 Xin Zhao Qiang He Shao-Cheng Lyu 《World Journal of Gastroenterology》 SCIE CAS 2025年第42期103-113,共11页
BACKGROUND Portosystemic venous invasion(PSVI)depth critically influences prognosis in borderline resectable pancreatic cancer(BRPC),necessitating precise preoperative discrimination for personalized therapy.AIM To de... BACKGROUND Portosystemic venous invasion(PSVI)depth critically influences prognosis in borderline resectable pancreatic cancer(BRPC),necessitating precise preoperative discrimination for personalized therapy.AIM To develop and validate a preoperative nomogram integrating computed to-mography parameters and carbohydrate antigen 19-9(CA19-9)kinetics for pre-dicting PSVI depth in treatment-naive BRPC.METHODS This retrospective cohort study analyzed 167 BRPC patients undergoing radical resection between 2011 and 2023.Patients were stratified by pathological PSVI depth[no venous invasion(VI)/adventitial/muscularis propria/intimal].Kaplan-Meier and ordinal logistic regression identified preoperative predictors from clinical/laboratory/computed tomography parameters(e.g.,circumferential involvement and CA19-9).A nomogram was developed and validated via cali-bration curves/decision curve analysis.RESULTS PSVI depth significantly stratified survival.:Intimal VI showed worst prognosis(median overall survival:9 months,5-year overall survival:0%vs no VI:17 months,12.5%;P<0.001).Independent predictors:CA19-9[odds ratio(OR)=3.819,Wald=14.125,95%confidence interval(CI):1.980-7.410],circumferential involvement(OR=8.271,Wald=33.352,95%CI:3.950-17.320),and luminal compromise(OR=3.544,Wald=8.489,95%CI:1.818-6.447).The nomogram achieved C-index=0.928(95%CI:0.889-0.967),with 100-250 points indicating high invasiveness risk.Decision curve analysis confirmed clinical utility(threshold:0-0.7).CONCLUSION This model integrates routine indicators to preoperatively quantify PSVI depth,guiding precision treatment. 展开更多
关键词 Borderline resectable pancreatic cancer Portosystemic venous invasion Pathological grading Predictive model Adventitial invasion Muscularis propria invasion Intimal invasion
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Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy:Existing challenges and proposed enhancements 认领 引用
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作者 Chao-Fan Sun Yu-Han Lin +3 位作者 Guo-Xing Ling Hui-Juan Gao Xing-Zhong Feng Chun-Quan Sun 《World Journal of Diabetes》 SCIE 2025年第4期270-283,共14页
BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive system... BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.METHODS A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October 2023.The included and excluded criteria were applied by the researchers to screen the literature.Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool.Disagreements were resolved through consultation with a third investigator.Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.Additionally,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.RESULTS The systematic review included 14 studies with a total of 26 models.The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938.All studies had high risks of bias,mainly due to participants,outcomes,and analysis.The most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood glucose.CONCLUSION The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of bias.Future studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice. 展开更多
关键词 Diabetic peripheral neuropathy Predictive models Systematic review Risk factors Prognostic risk
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Data-driven predictive model of coal permeability based on microscopic fracture structure characterization 认领 引用 被引量：2
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作者 Tianhao Yan Xiaomeng Xu +4 位作者 Jiafeng Liu Yihuai Zhang Muhammad Arif Xiaowei Xu Qiang Wang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第7期4476-4489,共14页
Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent he... Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent heterogeneity and complex internal structure of coal,a well-established method for predicting permeability based on microscopic fracture structures remains elusive.This paper presents a novel integrated approach that leverages the intrinsic relationship between microscopic fracture structure and permeability to construct a predictive model for coal permeability.The proposed framework encompasses data generation through the integration of three-dimensional(3D)digital core analysis and numerical simulations,followed by data-driven modeling via machine learning(ML)techniques.Key data-driven strategies,including feature selection and hyperparameter tuning,are employed to improve model performance.We propose and evaluate twelve data-driven models,including multilayer perceptron(MLP),random forest(RF),and hybrid methods.The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability.This method enables rapid estimation of coal permeability by inputting two-dimensional(2D)computed tomography images or parameters of the microscopic fracture structure,thereby providing an accurate and efficient means of permeability prediction. 展开更多
关键词 Microscopic fracture structure Lattice Boltzmann method Machine learning Coal permeability Predictive model
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Risk factors and predictive model for mortality in acute myocardial infarction with ventricular septal rupture at high altitudes 认领 引用 被引量：1
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作者 Li-Hong Zhang Zhi-Fu Cen +8 位作者 Qian Qiao Xue-Rui Ye Lu Cheng Gui-Qin Liu Yi Liu Xing-Qiang Zhang Xian-Feng Pan Hao-Ling Zhang Jing-Jing Zhang 《World Journal of Cardiology》 2025年第7期143-158,共16页
BACKGROUND Acute myocardial infarction(AMI)combined with ventricular septal perforation(VSR)is still a highly fatal condition in the era of reperfusion therapy.The incidence rate has decreased to 0.2%-0.4%due to the p... BACKGROUND Acute myocardial infarction(AMI)combined with ventricular septal perforation(VSR)is still a highly fatal condition in the era of reperfusion therapy.The incidence rate has decreased to 0.2%-0.4%due to the popularization of percutaneous coronary intervention.However,the risk is significantly increased for those who fail to undergo revascularization in time,and the mortality rate remains high.The current core contradiction in clinical practice lies in the selection of surgical timing,and the disparity in medical resources significantly affects prognosis.There is an urgent need to optimize the identification of high-risk populations and individualized treatment strategies.AIM To investigate the clinical features,determine the prognostic factors,and develop a predictive model for 30-day mortality in patients with acute myocardial infarction complicated by ventricular septal rupture(AMI-VSR)residing in high-altitude regions.METHODS This study retrospectively analyzed 48 AMI-VSR patients admitted to a Yunnan hospital from 2017 to 2024,with the establishment of survival(n=30)and mortality(n=18)groups based on patients’survival status.Risk factors were identified by univariate and multivariate logistic regression analyses.A nomogram model was developed using R software and validated via receiver operating characteristic(ROC)analysis and calibration curves.RESULTS Age,uric acid(UA),interleukin-6(IL-6),and low hemoglobin(Hb)were independent risk factors for 30-day mortality(odds ratios:1.147,1.006,1.034,and 0.941,respectively;P<0.05).The nomogram demonstrated excellent discrimination(area under the ROC curve=0.939)and calibration(Hosmer-Lemeshowχ²=2.268,P=0.971).In addition,patients’poor outcomes could be synergistically predicted by IL-6 and UA,advanced age,and reduced Hb.CONCLUSION This study highlights age,UA,IL-6,and Hb as critical predictors of mortality in AMI-VSR patients at high altitudes.The validated nomogram provides a practical tool for early risk stratification and tailored interventions,addressing gaps in managing this high-risk population in resource-limited settings. 展开更多
关键词 High-altitude regions Acute myocardial infarction complicated by ventricular septal rupture Mortality risk factors Nomogram predictive model
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