Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.

Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model

BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.

Real-Time Co-optimization of Gear Shifting and Engine Torque for Predictive Cruise Control of Heavy-Duty Trucks

Fuel consumption is one of the main concerns for heavy-duty trucks.Predictive cruise control(PCC)provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information.In this study,a real-time implementable PCC,which simultaneously optimizes engine torque and gear shifting,is proposed for heavy-duty trucks.To minimize fuel consumption,the problem of the PCC is formulated as a nonlinear model predictive control(MPC),in which the upcoming road elevation information is used.Finding the solution of the nonlinear MPC is time consuming;thus,a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method.Dynamic programming(DP)algorithm,as a global optimization algorithm,is used as a performance benchmark for the proposed solver.Simulation,hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller.The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution,with less than 1%deviation for testing roads.Moreover,the proposed co-optimization controller is implementable in a real-truck,and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9%without compromising the truck’s travel time.

Prediction Model-based Multi-objective Optimization for Mix-ratio Design of Recycled Aggregate Concrete

The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio of concrete.Then the compressive strength prediction model,the material cost,and environmental factors were simultaneously considered as objectives,while a multi-objective gray wolf optimization algorithm was developed for finding the optimal mix ratio.A total of 730 RAC datasets were used for training and testing the predication model,while the optimal design method for mix ratio was verified through RAC experiments.The experimental results show that the predicted,testing,and expected compressive strengths are nearly consistent,illustrating the effectiveness of the proposed method.

A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

Quantitative prediction model for the depth limit of oil accumulation in the deep carbonate rocks:A case study of Lower Ordovician in Tazhong area of Tarim Basin

With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.

Construction and validation of a risk-prediction model for anastomotic leakage after radical gastrectomy: A cohort study in China

Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.

Computational Fluid Dynamics Approach for Predicting Pipeline Response to Various Blast Scenarios: A Numerical Modeling Study

Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infras-tructure systems and networks capable of withstanding blast loading.Initially centered on high-profile facilities such as embassies and petrochemical plants,this concern now extends to a wider array of infrastructures and facilities.Engineers and scholars increasingly prioritize structural safety against explosions,particularly to prevent disproportionate collapse and damage to nearby structures.Urbanization has further amplified the reliance on oil and gas pipelines,making them vital for urban life and prime targets for terrorist activities.Consequently,there is a growing imperative for computational engineering solutions to tackle blast loading on pipelines and mitigate associated risks to avert disasters.In this study,an empty pipe model was successfully validated under contact blast conditions using Abaqus software,a powerful tool in mechanical engineering for simulating blast effects on buried pipelines.Employing a Eulerian-Lagrangian computational fluid dynamics approach,the investigation extended to above-surface and below-surface blasts at standoff distances of 25 and 50 mm.Material descriptions in the numerical model relied on Abaqus’default mechanical models.Comparative analysis revealed varying pipe performance,with deformation decreasing as explosion-to-pipe distance increased.The explosion’s location relative to the pipe surface notably influenced deformation levels,a key finding highlighted in the study.Moreover,quantitative findings indicated varying ratios of plastic dissipation energy(PDE)for different blast scenarios compared to the contact blast(P0).Specifically,P1(25 mm subsurface blast)and P2(50 mm subsurface blast)showed approximately 24.07%and 14.77%of P0’s PDE,respectively,while P3(25 mm above-surface blast)and P4(50 mm above-surface blast)exhibited lower PDE values,accounting for about 18.08%and 9.67%of P0’s PDE,respectively.Utilising energy-absorbing materials such as thin coatings of ultra-high-strength concrete,metallic foams,carbon fiber-reinforced polymer wraps,and others on the pipeline to effectively mitigate blast damage is recommended.This research contributes to the advancement of mechanical engineering by providing insights and solutions crucial for enhancing the resilience and safety of underground pipelines in the face of blast events.

Construction and evaluation of a liver cancer risk prediction model based on machine learning

BACKGROUND Liver cancer is one of the most prevalent malignant tumors worldwide,and its early detection and treatment are crucial for enhancing patient survival rates and quality of life.However,the early symptoms of liver cancer are often not obvious,resulting in a late-stage diagnosis in many patients,which significantly reduces the effectiveness of treatment.Developing a highly targeted,widely applicable,and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals.AIM To develop a liver cancer risk prediction model by employing machine learning techniques,and subsequently assess its performance.METHODS In this study,a total of 550 patients were enrolled,with 190 hepatocellular carcinoma(HCC)and 195 cirrhosis patients serving as the training cohort,and 83 HCC and 82 cirrhosis patients forming the validation cohort.Logistic regression(LR),support vector machine(SVM),random forest(RF),and least absolute shrinkage and selection operator(LASSO)regression models were developed in the training cohort.Model performance was assessed in the validation cohort.Additionally,this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve,calibration curve,and decision curve analysis(DCA)to determine the optimal predictive model for assessing liver cancer risk.RESULTS Six variables including age,white blood cell,red blood cell,platelet counts,alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR,SVM,RF,and LASSO regression models.The RF model exhibited superior discrimination,and the area under curve of the training and validation sets was 0.969 and 0.858,respectively.These values significantly surpassed those of the LR(0.850 and 0.827),SVM(0.860 and 0.803),LASSO regression(0.845 and 0.831),and ASAP(0.866 and 0.813)models.Furthermore,calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity.CONCLUSION The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.

Optimizing prediction models for pancreatic fistula after pancreatectomy:Current status and future perspectives

Postoperative pancreatic fistula(POPF)is a frequent complication after pancre-atectomy,leading to increased morbidity and mortality.Optimizing prediction models for POPF has emerged as a critical focus in surgical research.Although over sixty models following pancreaticoduodenectomy,predominantly reliant on a variety of clinical,surgical,and radiological parameters,have been documented,their predictive accuracy remains suboptimal in external validation and across diverse populations.As models after distal pancreatectomy continue to be pro-gressively reported,their external validation is eagerly anticipated.Conversely,POPF prediction after central pancreatectomy is in its nascent stage,warranting urgent need for further development and validation.The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance.Moreover,there is potential for the development of personalized prediction models based on patient-or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF.In the future,prospective multicenter studies and the integration of novel imaging technologies,such as artificial intelligence-based radiomics,may further refine predictive models.Addressing these issues is anticipated to revolutionize risk stratification,clinical decision-making,and postoperative management in patients undergoing pancre-atectomy.

Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension

BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

User Churn Prediction Hierarchical Model Based on Graph Attention Convolutional Neural Networks

The telecommunications industry is becoming increasingly aware of potential subscriber churn as a result of the growing popularity of smartphones in the mobile Internet era,the quick development of telecommunications services,the implementation of the number portability policy,and the intensifying competition among operators.At the same time,users'consumption preferences and choices are evolving.Excellent churn prediction models must be created in order to accurately predict the churn tendency,since keeping existing customers is far less expensive than acquiring new ones.But conventional or learning-based algorithms can only go so far into a single subscriber's data;they cannot take into consideration changes in a subscriber's subscription and ignore the coupling and correlation between various features.Additionally,the current churn prediction models have a high computational burden,a fuzzy weight distribution,and significant resource economic costs.The prediction algorithms involving network models currently in use primarily take into account the private information shared between users with text and pictures,ignoring the reference value supplied by other users with the same package.This work suggests a user churn prediction model based on Graph Attention Convolutional Neural Network(GAT-CNN)to address the aforementioned issues.The main contributions of this paper are as follows:Firstly,we present a three-tiered hierarchical cloud-edge cooperative framework that increases the volume of user feature input by means of two aggregations at the device,edge,and cloud layers.Second,we extend the use of users'own data by introducing self-attention and graph convolution models to track the relative changes of both users and packages simultaneously.Lastly,we build an integrated offline-online system for churn prediction based on the strengths of the two models,and we experimentally validate the efficacy of cloudside collaborative training and inference.In summary,the churn prediction model based on Graph Attention Convolutional Neural Network presented in this paper can effectively address the drawbacks of conventional algorithms and offer telecom operators crucial decision support in developing subscriber retention strategies and cutting operational expenses.

Development of a new irradiation-embrittlement prediction model for reactor pressure-vessel steels

Predicting the transition-temperature shift(TTS)induced by neutron irradiation in reactor pressure-vessel(RPV)steels is important for the evaluation and extension of nuclear power-plant lifetimes.Current prediction models may fail to properly describe the embrittlement trend curves of Chinese domestic RPV steels with relatively low Cu content.Based on the screened surveillance data of Chinese domestic and similar international RPV steels,we have developed a new fluencedependent model for predicting the irradiation-embrittlement trend.The fast neutron fluence(E>1 MeV)exhibited the highest correlation coefficient with the measured TTS data;thus,it is a crucial parameter in the prediction model.The chemical composition has little relevance to the TTS residual calculated by the fluence-dependent model.The results show that the newly developed model with a simple power-law functional form of the neutron fluence is suitable for predicting the irradiation-embrittlement trend of Chinese domestic RPVs,regardless of the effect of the chemical composition.

Prognostic prediction models for postoperative patients with stageⅠtoⅢcolorectal cancer based on machine learning

BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting survival.In addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.AIM To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢCRC.METHODS More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap validation.Important variables were screened with multivariable regression methods.Model performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model decision-making.Nomograms were constructed for practical model application.RESULTS Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was effective.The highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free survival.Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets.The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors.The nomograms were created.CONCLUSION We constructed a comprehensive,high-accuracy,important variable-based ML architecture for predicting the 3-category survival times.This architecture could serve as a vital reference for managing CRC patients.

Prediction model establishment and validation for enteral nutrition aspiration during hospitalization in patients with acute pancreatitis

BACKGROUND Acute pancreatitis(AP)is a disease caused by abnormal activation of pancreatic enzymes and can lead to self-digestion of pancreatic tissues and dysfunction of other organs.Enteral nutrition plays a vital role in the treatment of AP because it can meet the nutritional needs of patients,promote the recovery of intestinal function,and maintain the barrier and immune functions of the intestine.However,the risk of aspiration during enteral nutrition is high;once aspiration occurs,it may cause serious complications,such as aspiration pneumonia,and suffocation,posing a threat to the patient’s life.This study aims to establish and validate a prediction model for enteral nutrition aspiration during hospitalization in patients with AP.AIM To establish and validate a predictive model for enteral nutrition aspiration during hospitalization in patients with AP.METHODS A retrospective review was conducted on 200 patients with AP admitted to Chengdu Shangjin Nanfu Hospital,West China Hospital of Sichuan University from January 2020 to February 2024.Clinical data were collected from the electronic medical record system.Patients were randomly divided into a validation group(n=40)and a modeling group(n=160)in a 1:4 ratio,matched with 200 patients from the same time period.The modeling group was further categorized into an aspiration group(n=25)and a non-aspiration group(n=175)based on the occurrence of enteral nutrition aspiration during hospitalization.Univariate and multivariate logistic regression analyses were performed to identify factors influencing enteral nutrition aspiration in patients with AP during hospitalization.A prediction model for enteral nutrition aspiration during hospitalization was constructed,and calibration curves were used for validation.Receiver operating characteristic curve analysis was conducted to evaluate the predictive value of the model.RESULTS There was no statistically significant difference in general data between the validation and modeling groups(P>0.05).The comparison of age,gender,body mass index,smoking history,hypertension history,and diabetes history showed no statistically significant difference between the two groups(P>0.05).However,patient position,consciousness status,nutritional risk,Acute Physiology and Chronic Health Evaluation(APACHE-II)score,and length of nasogastric tube placement showed statistically significant differences(P<0.05)between the two groups.Multivariate logistic regression analysis showed that patient position,consciousness status,nutritional risk,APACHE-II score,and length of nasogastric tube placement were independent factors influencing enteral nutrition aspiration in patients with AP during hospitalization(P<0.05).These factors were incorporated into the prediction model,which showed good consistency between the predicted and actual risks,as indicated by calibration curves with slopes close to 1 in the training and validation sets.Receiver operating characteristic analysis revealed an area under the curve(AUC)of 0.926(95%CI:0.8889-0.9675)in the training set.The optimal cutoff value is 0.73,with a sensitivity of 88.4 and specificity of 85.2.In the validation set,the AUC of the model for predicting enteral nutrition aspiration in patients with AP patients during hospitalization was 0.902,with a standard error of 0.040(95%CI:0.8284-0.9858),and the best cutoff value was 0.73,with a sensitivity of 91.9 and specificity of 81.8.CONCLUSION A prediction model for enteral nutrition aspiration during hospitalization in patients with AP was established and demonstrated high predictive value.Further clinical application of the model is warranted.