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.

Development and application of hepatocellular carcinoma risk prediction model based on clinical characteristics and liver related indexes

BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagnosis.At present,no specific serolo-gical indicator or method to predict HCC,early diagnosis of HCC remains a challenge,especially in China,where the situation is more severe.AIM To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.METHODS The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected,using a retrospective study method.The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study.Based on the time of admission,the cases were divided into training cohort(n=1739)and validation cohort(n=467).Using HCC as a dependent variable,the research indicators were incorporated into logistic univariate and multivariate analysis.An HCC risk prediction model,which was called NSMC-HCC model,was then established in training cohort and verified in validation cohort.RESULTS Logistic univariate analysis showed that,gender,age,alpha-fetoprotein,and protein induced by vitamin K absence or antagonist-II,gamma-glutamyl transferase,aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC,alanine aminotransferase,total bilirubin and total bile acid were protective factors for HCC.When the cut-off value of the NSMC-HCC model joint prediction was 0.22,the area under receiver operating characteristic curve(AUC)of NSMC-HCC model in HCC diagnosis was 0.960,with sensitivity 94.40%and specificity 95.35%in training cohort,and AUC was 0.966,with sensitivity 90.00%and specificity 94.20%in validation cohort.In early-stage HCC diagnosis,the AUC of NSMC-HCC model was 0.946,with sensitivity 85.93%and specificity 93.62%in training cohort,and AUC was 0.947,with sensitivity 89.10%and specificity 98.49%in validation cohort.CONCLUSION The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.

A male-ABCD algorithm for hepatocellular carcinoma risk prediction in HBs Ag carriers

Objective: Hepatocellular carcinoma(HCC) development among hepatitis B surface antigen(HBs Ag) carriers shows gender disparity, influenced by underlying liver diseases that display variations in laboratory tests. We aimed to construct a risk-stratified HCC prediction model for HBs Ag-positive male adults.Methods: HBs Ag-positive males of 35-69 years old(N=6,153) were included from a multi-center populationbased liver cancer screening study. Randomly, three centers were set as training, the other three centers as validation. Within 2 years since initiation, we administrated at least two rounds of HCC screening using Bultrasonography and α-fetoprotein(AFP). We used logistic regression models to determine potential risk factors,built and examined the operating characteristics of a point-based algorithm for HCC risk prediction.Results: With 2 years of follow-up, 302 HCC cases were diagnosed. A male-ABCD algorithm was constructed including participant's age, blood levels of GGT(γ-glutamyl-transpeptidase), counts of platelets, white cells,concentration of DCP(des-γ-carboxy-prothrombin) and AFP, with scores ranging from 0 to 18.3. The area under receiver operating characteristic was 0.91(0.90-0.93), larger than existing models. At 1.5 points of risk score,26.10% of the participants in training cohort and 14.94% in validation cohort were recognized at low risk, with sensitivity of identifying HCC remained 100%. At 2.5 points, 46.51% of the participants in training cohort and 33.68% in validation cohort were recognized at low risk with 99.06% and 97.78% of sensitivity, respectively. At 4.5 points, only 20.86% of participants in training cohort and 23.73% in validation cohort were recognized at high risk,with positive prediction value of 22.85% and 12.35%, respectively.Conclusions: Male-ABCD algorithm identified individual's risk for HCC occurrence within short term for their HCC precision surveillance.

Risk prediction models for hepatocellular carcinoma in different populations

Hepatocellular carcinoma （HCC） is a malignant disease with limited therapeutic options due to its aggressive progression. It places heaW burden on most low and middle income countries to treat HCC patients. Nowadays accurate HCC risk predictions can help making decisions on the need for HCC surveillance and antiviral therapy. HCC risk prediction models based on major risk factors of HCC are useful and helpful in providing adequate surveillance strategies to individuals who have different risk levels. Several risk prediction models among cohorts of different populations for estimating HCC incidence have been presented recently by using simple, efficient, and ready-to-use parameters. Moreover, using predictive scoring systems to assess HCC development can provide suggestions to improve clinical and public health approaches, making them more cost-effective and effort-effective, for inducing personalized surveillance programs according to risk stratification. In this review, the features of risk prediction models of HCC across different populations were summarized, and the perspectives of HCC risk prediction models were discussed as well.

Risk Prediction of Aortic Dissection Operation Based on Boosting Trees

During the COVID-19 pandemic,the treatment of aortic dissection has faced additional challenges.The necessary medical resources are in serious shortage,and the preoperative waiting time has been significantly prolonged due to the requirement to test for COVID-19 infection.In this work,we focus on the risk prediction of aortic dissection surgery under the influence of the COVID-19 pandemic.A general scheme of medical data processing is proposed,which includes five modules,namely problem definition,data preprocessing,data mining,result analysis,and knowledge application.Based on effective data preprocessing,feature analysis and boosting trees,our proposed fusion decision model can obtain 100%accuracy for early postoperative mortality prediction,which outperforms machine learning methods based on a single model such as LightGBM,XGBoost,and CatBoost.The results reveal the critical factors related to the postoperative mortality of aortic dissection,which can provide a theoretical basis for the formulation of clinical operation plans and help to effectively avoid risks in advance.

Risk prediction of common bile duct stone recurrence based on new common bile duct morphological subtypes

Stones in the common bile duct(CBD) are reported worldwide, and this condition is majorly managed through endoscopic retrograde cholangiopancreatography(ERCP). CBD stone recurrence is an important issue after endoscopic stone removal. Therefore, it is essential to identify its risk factors to determine the necessity of regular follow-up in patients who underwent endoscopic removal of CBD stones. The authors identified that the S and polyline morphological subtypes of CBD were associated with increased stone recurrence. New morphological subtypes of CBD presented by the authors can be important risk predictors of recurrence after endoscopic stone removal. Furthermore, the new morphological subtypes of CBD may predict the risk of residual CBD stones or technical difficulty in CBD stone removal. Further studies with a large sample size and longer follow-up durations are warranted to examine the usefulness of the newly identified morphological subtypes of CBD in predicting the outcomes of ERCP for CBD stone removal.

A Readmission Risk Prediction Model for Elderly Patients with Coronary Heart Disease

Objective:To analyze the independent risk factors and establish a risk prediction model by investigating the readmission of elderly patients with coronary heart disease(CHD)within 1 year after discharge.Methods:A total of 480 CHD patients,who were hospitalized in the Affiliated Hospital of Hebei University from October 2019 to December 2020,were included in this study.A general data scale,mental health status scale,the Clinical Frailty Scale,Pittsburgh Sleep Quality Index,as well as the Family Adaptability and Cohesion Evaluation Scale were used to collect data.According to the number of readmissions due to CHD within 1 year after discharge,the patients were divided into two groups:the readmission group(n=212)and the no readmission group(n=268).General data,laboratory examination indicators,frailty,mental health status,sleep status,as well as family intimacy and adaptability were compared between the two groups.Logistic regression was used to analyze the independent risk factors for the readmission of these patients,and R software was used to construct a line diagram model for predicting readmission of elderly patients with CHD.Results:Five factors including body mass index(OR=1.045),low density lipoprotein(OR=1.123),frailty(OR=1.946),mental health(OR=1.099),as well as family intimacy and adaptability(OR=0.928)were included to construct the risk prediction model for the readmission of elderly patients with CHD within 1 year after discharge.The ROC curve showed that the area under the curve for predicting readmission of elderly patients with CHD was 0.816;Hosmer-Lemeshow goodness of fit test showed X2=1.456 and P=0.989;the maximum Youden index corresponding to the predicted value of risk was 0.526.The results showed that the model could accurately predict the risk of readmission in elderly patients with CHD within 1 year after discharge.Conclusion:This study constructed a line diagram model based on five independent risk factors of the readmission of elderly patients with CHD:body mass index,low density lipoprotein,frailty,mental health status,as well as family intimacy and adaptability.This model has good discrimination,accuracy,and predictive efficiency,providing reference for the early prevention and intervention of readmission in elderly patients with CHD recurrence.

Clinical characteristics and mortality risk prediction model in children with acute myocarditis

Background Acute myocarditis(AMC)can cause poor outcomes or even death in children.We aimed to identify AMC risk factors and create a mortality prediction model for AMC in children at hospital admission.Methods This was a single-center retrospective cohort study of AMC children hospitalized between January 2016 and January 2020.The demographics,clinical examinations,types of AMC,and laboratory results were collected at hospital admission.In-hospital survival or death was documented.Clinical characteristics associated with death were evaluated.Results Among 67 children,51 survived,and 16 died.The most common symptom was digestive disorder(67.2%).Based on the Bayesian model averaging and Hosmer–Lemeshow test,we created a final best mortality prediction model(acute myocarditis death risk score,AMCDRS)that included ten variables(male sex,fever,congestive heart failure,left-ventricular ejection fraction<50%,pulmonary edema,ventricular tachycardia,lactic acid value>4,fulminant myocarditis,abnormal creatine kinase-MB,and hypotension).Despite differences in the characteristics of the validation cohort,the model discrimination was only marginally lower,with an AUC of 0.781(95%confidence interval=0.675–0.852)compared with the derivation cohort.Model calibration likewise indicated acceptable fit(Hosmer‒Lemeshow goodness-of-fit,P¼=0.10).Conclusions Multiple factors were associated with increased mortality in children with AMC.The prediction model AMCDRS might be used at hospital admission to accurately identify AMC in children who are at an increased risk of death.

Minimal improvement in coronary artery disease risk prediction in Chinese population using polygenic risk scores:evidence from the China Kadoorie Biobank

Background:Several studies have reported that polygenic risk scores(PRSs)can enhance risk prediction of coronary artery disease(CAD)in European populations.However,research on this topic is far from sufficient in non-European countries,including China.We aimed to evaluate the potential of PRS for predicting CAD for primary prevention in the Chinese population.Methods:Participants with genome-wide genotypic data from the China Kadoorie Biobank were divided into training(n=28,490)and testing sets(n=72,150).Ten previously developed PRSs were evaluated,and new ones were developed using clumping and thresholding or LDpred method.The PRS showing the strongest association with CAD in the training set was selected to further evaluate its effects on improving the traditional CAD risk-prediction model in the testing set.Genetic risk was computed by summing the product of the weights and allele dosages across genome-wide single-nucleotide polymorphisms.Prediction of the 10-year first CAD events was assessed using hazard ratios(HRs)and measures of model discrimination,calibration,and net reclassification improvement(NRI).Hard CAD(nonfatal I21-I23 and fatal I20-I25)and soft CAD(all fatal or nonfatal I20-I25)were analyzed separately.Results:In the testing set,1214 hard and 7201 soft CAD cases were documented during a mean follow-up of 11.2 years.The HR per standard deviation of the optimal PRS was 1.26(95%CI:1.19-1.33)for hard CAD.Based on a traditional CAD risk prediction model containing only non-laboratory-based information,the addition of PRS for hard CAD increased Harrell’s C index by 0.001(-0.001 to 0.003)in women and 0.003(0.001 to 0.005)in men.Among the different high-risk thresholds ranging from 1%to 10%,the highest categorical NRI was 3.2%(95%CI:0.4-6.0%)at a high-risk threshold of 10.0%in women.The association of the PRS with soft CAD was much weaker than with hard CAD,leading to minimal or no improvement in the soft CAD model.Conclusions:In this Chinese population sample,the current PRSs minimally changed risk discrimination and offered little improvement in risk stratification for soft CAD.Therefore,this may not be suitable for promoting genetic screening in the general Chinese population to improve CAD risk prediction.

Machine learning for carbonate formation drilling: Mud loss prediction using seismic attributes and mud loss records

Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.

Development and validation of a nomogram model for predicting the risk of pre-hospital delay in patients with acute myocardial infarction

BACKGROUND Acute myocardial infarction(AMI)is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium.Timely medical contact is critical for successful AMI treatment,and delays increase the risk of death for patients.Pre-hospital delay time(PDT)is a significant challenge for reducing treatment times,as identifying high-risk patients with AMI remains difficult.This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care,ultimately reducing PDT and improving treatment outcomes.AIM To construct a nomogram model for forecasting pre-hospital delay(PHD)likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk.METHODS A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022.The study included 252 patients,with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio.Independent risk factors influencing PHD were identified in the development group,leading to the establishment of a nomogram model for predicting PHD in patients with AMI.The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups.RESULTS Independent risk factors for PHD in patients with AMI included living alone,hyperlipidemia,age,diabetes mellitus,and digestive system diseases(P<0.05).A characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787(95%confidence interval:0.716–0.858)and 0.770(95%confidence interval:0.660-0.879)in the development and validation groups,respectively,demonstrating the model's good discriminatory ability.The Hosmer–Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts(P>0.05),indicating satisfactory model calibration.CONCLUSION The nomogram model,developed with independent risk factors,accurately forecasts PHD likelihood in AMI individuals,enabling efficient identification of PHD risk in these patients.

An intelligent approach for flight risk prediction under icing conditions

Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions,especially aircraft icing conditions.The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector(commanded airspeed,commanded bank angle,and commanded vertical velocity)is developed to provide the crew with practical risk information.However,the construction of flight risk space by means of computational flight dynamics suffers from certain defects,including slow computing speed.Accordingly,an intelligent approach for flight risk prediction is proposed to address these defects based on neural networks.Radial Basis Function Neural Network(RBFNN)is optimized using Adaptive Particle Swarm Optimization(APSO).To optimize both the parameters and the structure of APSO-RBFNN,a fitness function containing the training accuracy and network structure size is proposed.Extensive experimental results demonstrate that the flight risk predicted by APSO-RBFNN is very close to that obtained via computational flight dynamics.The average error(RMSE)is less than 10^(-1).The approach achieves a speedup close to 1000x compared with computational flight dynamics.In addition,some flight upset and recovery cases are presented to illustrate the efficiency of the intelligent approach for flight risk prediction.

Navigating breast cancer brain metastasis:Risk factors,prognostic indicators,and treatment perspectives

In this editorial,we comment on the article by Chen et al.We specifically focus on the risk factors,prognostic factors,and management of brain metastasis(BM)in breast cancer(BC).BC is the second most common cancer to have BM after lung cancer.Independent risk factors for BM in BC are:HER-2 positive BC,triplenegative BC,and germline BRCA mutation.Other factors associated with BM are lung metastasis,age less than 40 years,and African and American ancestry.Even though risk factors associated with BM in BC are elucidated,there is a lack of data on predictive models for BM in BC.Few studies have been made to formulate predictive models or nomograms to address this issue,where age,grade of tumor,HER-2 receptor status,and number of metastatic sites(1 vs>1)were predictive of BM in metastatic BC.However,none have been used in clinical practice.National Comprehensive Cancer Network recommends screening of BM in advanced BC only when the patient is symptomatic or suspicious of central nervous system symptoms;routine screening for BM in BC is not recommended in the guidelines.BM decreases the quality of life and will have a significant psychological impact.Further studies are required for designing validated nomograms or predictive models for BM in BC;these models can be used in the future to develop treatment approaches to prevent BM,which improves the quality of life and overall survival.

Multicenter study of the clinicopathological features and recurrence risk prediction model of early-stage breast cancer with low-positive human epidermal growth factor receptor 2 expression in China (Chinese Society of Breast Surgery 021)

Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors.Methods:Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association,from January 2015 to December 2016 were collected.The prognostic factors of these patients were analyzed.Results:Twenty-nine hospitals provided valid cases.From 2015 to 2016,a total of 25,096 cases of early-stage breast cancer were treated,7642(30.5%)of which had low-positive HER2 expression and were included in the study.After ineligible cases were excluded,6486 patients were included in the study.The median follow-up time was 57 months(4-76 months).The disease-free survival rate was 92.1%at 5 years,and the overall survival rate was 97.4%at 5 years.At the follow-up,506(7.8%)cases of metastasis and 167(2.6%)deaths were noted.Multivariate Cox regression analysis showed that tumor stage,lymphvascular invasion,and the Ki67 index were related to recurrence and metastasis(P<0.05).The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815(95%confidence interval:0.750-0.880).Conclusions:Early-stage breast cancer patients with low-positive HER2 expression account for 30.5%of all patients.Tumor stage,lymphvascular invasion,and the Ki67 index are factors affecting prognosis.The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years.Trial registration:ChiCTR.org.cn,ChiCTR2100046766.

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia,usage of diabetes drugs,changes in insulin levels,and excretion,and this risk begins as early as adolescence.Many factors including demographic data(such as age,height,weight,and gender),medical history(such as smoking,drinking,and menopause),and examination(such as bone mineral density,blood routine,and urine routine)may be related to bone metabolism in patients with diabetes.However,most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans.In addition,the fracture risk of patients with diabetes and osteoporosis has not been further studied previously.In this paper,a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis,and investigate the effect of patients’physiological factors on fracture risk.A total of 147 raw input features are considered in our model.The presented model is compared with several benchmarks based on various metrics to prove its effectiveness.Moreover,the top 18 influencing factors of fracture risks of patients with diabetes are determined.

Polygenic risk scores:the future of cancer risk prediction,screening,and precision prevention

Genome-wide association studies(GWASs)have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers.The genetic variants associated with a cancer can be combined into a polygenic risk score(PRS),which captures part of an individual’s genetic susceptibility to cancer.Recently,PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer,which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk.In this context,we provide an overview of the major discoveries from cancer GWASs.We then review the methodologies used for PRS construction,and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors.Potential utility of PRSs in cancer risk prediction,screening,and precision prevention are illustrated.Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.

Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma(HCC).However,studies indicate that nearly 70%of patients experience HCC recurrence within five years following hepatectomy.The earlier the recurrence,the worse the prognosis.Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data,which are lagging.Hence,developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis.AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC.METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study.These data were randomly divided into training and test sets at a ratio of 8:2.The training set was analyzed,and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models.Each model was evaluated,and the bestperforming model was selected for interpreting the importance of each variable.Finally,an online calculator based on the model was generated for daily clinical practice.RESULTS Following machine learning analysis,eight key feature variables(age,intratumoral arteries,alpha-fetoprotein,preoperative blood glucose,number of tumors,glucose-to-lymphocyte ratio,liver cirrhosis,and pre-operative platelets)were selected to construct six different prediction models.The XGBoost model outperformed other models,with the area under the receiver operating characteristic curve in the training,validation,and test datasets being 0.993(95%confidence interval:0.982-1.000),0.734(0.601-0.867),and 0.706(0.585-0.827),respectively.Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value.CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence.This model may guide surgical strategies and postoperative individualized medicine.

Robust dynamic risk prediction with longitudinal studies

Providing accurate and dynamic age-specific risk prediction is a crucial step in precision medicine.In this manuscript,we introduce an approach for estimating theτ-year age-specific absolute riskdirectly via a flexible varying coefficient model.The approach facilitates the utilisation of predictors varying over an individual’s lifetime.By using a nonparametric inverse probability weightedkernel estimating equation,the age-specific effects of risk factors are estimated without requiring the specification of the functional form.The approach allows borrowing information acrossindividuals of similar ages,and therefore provides a practical solution for situations where the longitudinal information is only measured sparsely.We evaluate the performance of the proposedestimation and inference procedures with numerical studies,and make comparisons with existingmethods in the literature.We illustrate the performance of our proposed approach by developinga dynamic prediction model using data from the Framingham Study.

Deep Learning-Based Prediction of Traffic Accidents Risk for Internet of Vehicles

With the increasing number of vehicles,traffic accidents pose a great threat to human lives.Hence,aiming at reducing the occurrence of traffic accidents,this paper proposes an algorithm based on a deep convolutional neural network and a random forest to predict accident risks.Specifically,the proposed algorithm includes a feature extractor and a feature classifier,where the former extracts key features using a convolutional neural network and the latter outputs a probability value of traffic accidents using a random forest with multiple decision trees,which indicates the degree of accident risks.Simulations show that the proposed algorithm can achieve higher performance in terms of the Area Under the Curve(AUC)of the Receiver Characteristic Operator as well as accuracy than the existing algorithms based on the Adaboost or the pure convolutional neural networks.