Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
Soil mineralized nitrogen(N)is a vital component of soil N supply capacity and an important N source for rice growth.Unveiling N mineralization(Nm)process characteristics and developing a simple and effective approach...Soil mineralized nitrogen(N)is a vital component of soil N supply capacity and an important N source for rice growth.Unveiling N mineralization(Nm)process characteristics and developing a simple and effective approach to evaluate soil Nm are imperative to guide N fertilizer application and enhance its efficiency in various paddy soils with different physicochemical properties.Soil properties are important driving factors contributing to soil Nm differences and must be considered to achieve effective N management.Nevertheless,discrepancies in Nm capacity and other key influencing factors remain uncertain.To address this knowledge gap,this study collected 52 paddy soil samples from Taihu Lake Basin,China,which possess vastly different physicochemical properties.The samples were subjected to a 112-d submerged anaerobic incubation experiment at a constant temperature to obtain the soil Nm characteristics.Reaction kinetics models,including one-pool exponential model,two-pool exponential model,and effective cumulative temperature model,were employed to compare characteristic differences between Nm potential(Nmp)and short-term accumulated mineralized N(Amn)processes in relation to soil physicochemical properties.Based on these relationships,simplified Nmp prediction methods for paddy soils were established.The results revealed that the Nmp values were 145.18,88.64,and 21.03 mg kg-1 in paddy soils with pH<6.50,6.50≤pH≤7.50,and pH>7.50,respectively.Significantly,short-term Amn at day 14 showed a good correlation(P<0.01)with Nmp(R2=0.94),indicating that the prevailing short-term incubation experiment is an acceptable marker for Nmp.Moreover,Nmp correlated well with the ultraviolet absorbance value at 260 nm based on NaHCO3 extraction(Na260),further streamlining the Nmp estimation method.The incorporation of easily obtainable soil properties,including pH,total N(TN),and the ratio of total organic carbon to TN(C/N),alongside Na260 for Nmp evaluation allowed the multiple regression model,Nmp=58.62×TN-23.18×pH+13.08×C/N+86.96×Na260,to achieve a high prediction accuracy(R2=0.95).The reliability of this prediction was further validated with published data of paddy soils in the same region and other rice regions,demonstrating the regional applicability and prospects of this model.This study underscored the roles of soil properties in Nm characteristics and mechanisms and established a site-specific prediction model based on rapid extractions and edaphic properties of paddy soils,paving the way for developing rapid and precise Nm prediction models.展开更多
This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et...This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There i...The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.展开更多
BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk...BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.展开更多
BACKGROUND Diagnosis of inflammatory bowel disease and assessment of disease activity are fundamentally reliant on endoscopy.Nonetheless,it is costly and invasive,highlighting the necessity for more accessible and non...BACKGROUND Diagnosis of inflammatory bowel disease and assessment of disease activity are fundamentally reliant on endoscopy.Nonetheless,it is costly and invasive,highlighting the necessity for more accessible and non-invasive biomarkers to assist in the diagnosis and evaluation of inflammatory bowel disease.AIM To examine the correlation of biomarkers with endoscopic activity,evaluate their diagnostic significance,and develop models to forecast endo-scopic activity.METHODS We performed a retrospective single-center analysis of 365 patients with ulcerative colitis(UC),319 with Crohn’s disease(CD)and 100 controls at the First Affiliated Hospital of Zhengzhou University from January 2022 to September 2024.The following biomarkers were analyzed:White blood cell,hemoglobin(Hb),platelet(PLT),neutrophil(N),lymphocyte(L),hematocrit(HCT),eosinophil,albumin(ALB),globulin(GLB),C-reactive protein(CRP),erythrocyte sedimentation rate(ESR),ALB/GLB(AGR),CRP/ALB(CAR),CRP/L(CLR),PLT/ALB(PAR),PLT/L(PLR),and N/L(NLR).RESULTS Serum N,PLT,GLB,CRP,ESR,CAR,CLR,PLR,PAR,and NLR levels were significantly elevated(P<0.001 or P<0.05)in the UC and CD groups compared to controls,whereas Hb,HCT,L,ALB,and AGR were reduced(P<0.001 or P<0.05).Aside from L and eosinophil,substantial differences were observed between mild and severe activity in UC and CD(P<0.001 or P<0.05).UC and CD patients who exhibited an endoscopic response after 14 weeks of treatment had elevated CRP,CAR,and CLR levels at baseline compared to endoscopic nonresponders(P<0.01 or P<0.05).The UC nomogram model utilizing ESR,CAR,and PAR,along with the CD nomogram model employing AGR and PAR,demonstrate predictive significance and clinical applicability for assessing endoscopic activity.CONCLUSION White blood cell,Hb,HCT,PLT,N,CRP,ESR,ALB,GLB,AGR,CAR,CLR,PLR,PAR and NLR are significantly correlated with the endoscopic activity of UC and CD.Patients with UC and CD exhibiting elevated CRP,CAR,and CLR levels are more inclined to respond to treatment.Our nomogram models can precisely forecast endoscopic activity.展开更多
The urgent necessity for enhanced risk stratification to improve the efficiency of colonoscopy screening is underscored by the fact that colorectal cancer(CRC)continues to be a primary cause of global cancer mortality...The urgent necessity for enhanced risk stratification to improve the efficiency of colonoscopy screening is underscored by the fact that colorectal cancer(CRC)continues to be a primary cause of global cancer mortality.Conventional models mostly rely on generalized obesity markers including body mass index(BMI),which does not effectively represent oncogenic risk linked with abdominal obesity.Liu et al undertook a large-scale case-control study comprising 6484 firsttime colonoscopy patients at a prominent Chinese hospital between 2020 and 2023 to overcome this restriction.Age,male sex,smoking status,and raised waist-hip ratio(WHR)were found by multivariate logistic regression as independent predictors of advanced colorectal neoplasia(ACN).In a validation cohort of 1891 individuals,a new 7-point risk scoring model was created and stratified into low-(5.0%)ACN prevalence,moderate-(10.3%)and high-risk(17.6%).With C-statistic=0.66 the model showed better discriminating ability than the Asia-Pacific Colorectal Screening(APCS)score(C-statistic=0.63)and the BMI-modified APCS model.These results fit newly published data showing central obesity as a major carcinogenic driver via pro-inflammatory visceral adipokine channels.With the use of WHR,patient risk classification is greatly improved,providing a practical tool to make the most of screening resources in the face of rising CRC incidence rates.Finally,multi-ethnic validation is necessary for the WHR-based scoring model to be considered for integration into global CRC preventive frameworks,since it improves the accuracy of ACN risk prediction.展开更多
BACKGROUND Female depression is a prevalent and increasingly recognized mental health issue.Due to cultural and social factors,many female patients still face challenges in diagnosis and treatment,and traditional asse...BACKGROUND Female depression is a prevalent and increasingly recognized mental health issue.Due to cultural and social factors,many female patients still face challenges in diagnosis and treatment,and traditional assessment methods often fail to identify high-risk individuals accurately.This highlights the necessity of developing more precise predictive tools.Utilizing machine learning(ML)algorithms to construct predictive models may overcome the limitations of traditional methods,providing more comprehensive support for women’s mental health.AIM To construct an ML-nomogram hybrid model that translates multivariate risk predictors of female depressive symptoms into actionable clinical scoring thresholds,optimizing predictive accuracy and interpretability for healthcare applications.METHODS We analyzed data from 7609 female participants aged 18 to 85 years from the Guangdong Provincial Sleep and Psychosomatic Health Survey.Sixteen variables,including anxiety symptoms,insomnia,chronic diseases,exercise habits,and age,were selected based on prior literature and comprehensively incorporated into ML models to maximize predictive information utilization.Three ML algorithms,extreme gradient boosting,support vector machine,and light gradient boosting machine,were employed to construct predictive models.Model performance was evaluated using accuracy,precision,recall,F1 score,and area under the curve(AUC).Feature importance was interpreted using SHapley Additive exPlanations(SHAP),with ablation studies validating the impact of the top five SHAPderived features on predictive performance,and a nomogram was constructed based on these prioritized predictors.Clinical utility was assessed through decision curve analysis.RESULTS The prevalence of depressive symptoms was 6.8%among the sample.The evaluation of predictive models revealed that light gradient boosting machine achieved a top-performing AUC of 0.867,placing it ahead of extreme gradient boosting(AUC=0.862)and support vector machine(AUC=0.849).SHAP analysis identified insomnia,anxiety symptoms,age,chronic disease,and exercise as the top five predictors.The nomogram based on these features demonstrated excellent discrimination(AUC=0.910)and calibration,with significant net benefits in decision curve analysis compared to baseline strategies.The model effectively stratifies depressive symptoms risk,facilitating personalized and quantitative assessments in clinical settings.We also developed an interactive digital version of the nomogram to facilitate its application in clinical practice.CONCLUSION The ML-based model effectively predicts depressive symptoms in women,identifying insomnia,anxiety symptoms,age,chronic diseases,and exercise as key predictors,offering a practical tool for early detection and intervention.展开更多
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
In this article,we comment on the article published by Yu et al.By employing LASSO regression and Cox proportional hazard models,the article identified nine significant variables affecting survival,including body mass...In this article,we comment on the article published by Yu et al.By employing LASSO regression and Cox proportional hazard models,the article identified nine significant variables affecting survival,including body mass index,Karnofsky performance status,and tumor-node-metastasis staging.We firmly concur with Yu et al regarding the vital significance of clinical prediction models(CPMs),including logistic regression and Cox regression for assessment in esophageal carcinoma(EC).However,the nomogram's limitations and the complexities of integrating genetic factors pose challenges.The integration of immunological data with advanced statistics offers new research directions.High-throughput sequencing and big data,facilitated by machine learning,have revolutionized cancer research but require substantial computational resources.The future of CPMs in EC depends on leveraging these technologies to improve predictive accuracy and clinical application,addressing the need for larger datasets,patientreported outcomes,and regular updates for clinical relevance.展开更多
BACKGROUND Few studies have specifically modeled the risk of venous thromboembolism(VTE)for postoperative hepatocellular carcinoma(HCC)patients,although HCC is the third leading cause of cancer death worldwide.This st...BACKGROUND Few studies have specifically modeled the risk of venous thromboembolism(VTE)for postoperative hepatocellular carcinoma(HCC)patients,although HCC is the third leading cause of cancer death worldwide.This study aimed to develop and validate a nomogram that accurately predicts the risk of VTE in patients after HCC surgery.AIM To develop and validate a nomogram to accurately predict the risk of VTE in postoperative HCC patients by integrating clinical and laboratory risk factors.The model seeks to provide a user-friendly tool for identifying high-risk individuals who may benefit from targeted anticoagulation therapy,thereby improving clinical decision-making and patient outcomes.METHODS Data from patients who underwent HCC surgery at Chongqing University Cancer Hospital in China were analyzed.Through univariate and multivariate logistic regression analyses,independent risk factors for VTE were identified and integrated into a nomogram.The predictive performance of the nomogram was assessed via receiver operating characteristic curves,calibration curves,decision curve analysis and other relevant metrics.RESULTS Of 905 postoperative HCC patients were included in the study.The nomogram incorporated eight independent risk factors for VTE:Karnofsky Performance Scale,base disease,cancer stage(tumor-node-metastasis),chemotherapy,D-dimer concentration,white blood cell count,hemoglobin,and fibrinogen.The C-index for the nomogram model was 0.825 in the training cohort and 0.820 in the validation cohort,indicating good discriminative ability.Calibration plots of the model revealed high concordance between the predicted probabilities and observed outcomes.CONCLUSION We developed and validated a novel nomogram that can accurately estimate the risk of VTE in individual postoperative HCC patients.This model can identify high-risk patients who may benefit from targeted anticoagulation therapy.展开更多
BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high...BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.展开更多
Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Pat...Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.展开更多
Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials t...Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions.These scores aim to provide a structured framework to support clinical judgment.However,their effectiveness varies across patient populations,and their predictive accuracy remains inconsistent.In this review,we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation.While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted,their sensitivity and specificity often fall short in complex clinical settings.Factors such as underlying disease pathophysiology,patient characteristics,and clinician subjectivity impact score performance and reliability.Moreover,disparities in validation across diverse populations limit generalizability.With growing interest in artificial intelligence(AI)and machine learning,there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles.However,current AI approaches face challenges related to interpretability,bias,and ethical implementation.This paper underscores the need for more robust,individualized,and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.展开更多
Colorectal cancer(CRC)is a prevalent malignancy,with surgery playing a key role in its treatment.However,perioperative complications,such as anastomotic leaks,infections,and mortality,can significantly affect surgical...Colorectal cancer(CRC)is a prevalent malignancy,with surgery playing a key role in its treatment.However,perioperative complications,such as anastomotic leaks,infections,and mortality,can significantly affect surgical outcomes,extend hospital stays,and increase healthcare costs.Traditional risk prediction models often lack precision,leading to increased interest in artificial intelligence(AI)for improving risk stratification.This review examines the application of AI,particularly machine learning and deep learning,in predicting perioperative complications in CRC surgery.AI models have been employed to predict a variety of postoperative complications,including readmissions,surgical-site infections,anastomotic leakage,and mortality,by analyzing diverse data sources such as electronic health records,medical imaging,and preoperative markers.Despite the promising results,several challenges remain,including data quality,model generalizability,the complexity of clinical data,and ethical and regulatory concerns.The review emphasizes the need for multicenter,diverse datasets and the integration of AI into clinical workflows to improve model performance and adoption.Future efforts should focus on enhancing the transparency and interpretability of AI models to ensure their successful implementation in clinical practice,ultimately improving patient outcomes and surgical decision-making in CRC surgery.展开更多
Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of c...Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.展开更多
Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine ...Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.展开更多
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
In this article,we discuss the study by Cheng et al,published in the World Journal of Gastroenterology,focusing on predictive methods for post-hepatectomy liver failure(PHLF).PHLF is a common and serious complication,...In this article,we discuss the study by Cheng et al,published in the World Journal of Gastroenterology,focusing on predictive methods for post-hepatectomy liver failure(PHLF).PHLF is a common and serious complication,and accurate prediction is critical for clinical management.The study examines the potential of ultrasound elastography and splenic size in predicting PHLF.Ultrasound elastography reflects liver functional reserve,while splenic size provides additional predictive value.By integrating these factors with serological markers,we developed a comprehensive prediction model that effectively stratifies patient risk and supports personalized clinical decisions.This approach offers new insights into predicting PHLF.These methods not only assist clinicians in identifying high-risk patients earlier but also provide scientific support for personalized treatment strategies.Future research will aim to validate the model's accuracy with larger sample sizes,further enhancing the clinical application of these non-invasive indicators.展开更多
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金supported by the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.Y201956)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(No.2023QNRC001)the National Key Research and Development Program of China(No.2017YFD200104).
文摘Soil mineralized nitrogen(N)is a vital component of soil N supply capacity and an important N source for rice growth.Unveiling N mineralization(Nm)process characteristics and developing a simple and effective approach to evaluate soil Nm are imperative to guide N fertilizer application and enhance its efficiency in various paddy soils with different physicochemical properties.Soil properties are important driving factors contributing to soil Nm differences and must be considered to achieve effective N management.Nevertheless,discrepancies in Nm capacity and other key influencing factors remain uncertain.To address this knowledge gap,this study collected 52 paddy soil samples from Taihu Lake Basin,China,which possess vastly different physicochemical properties.The samples were subjected to a 112-d submerged anaerobic incubation experiment at a constant temperature to obtain the soil Nm characteristics.Reaction kinetics models,including one-pool exponential model,two-pool exponential model,and effective cumulative temperature model,were employed to compare characteristic differences between Nm potential(Nmp)and short-term accumulated mineralized N(Amn)processes in relation to soil physicochemical properties.Based on these relationships,simplified Nmp prediction methods for paddy soils were established.The results revealed that the Nmp values were 145.18,88.64,and 21.03 mg kg-1 in paddy soils with pH<6.50,6.50≤pH≤7.50,and pH>7.50,respectively.Significantly,short-term Amn at day 14 showed a good correlation(P<0.01)with Nmp(R2=0.94),indicating that the prevailing short-term incubation experiment is an acceptable marker for Nmp.Moreover,Nmp correlated well with the ultraviolet absorbance value at 260 nm based on NaHCO3 extraction(Na260),further streamlining the Nmp estimation method.The incorporation of easily obtainable soil properties,including pH,total N(TN),and the ratio of total organic carbon to TN(C/N),alongside Na260 for Nmp evaluation allowed the multiple regression model,Nmp=58.62×TN-23.18×pH+13.08×C/N+86.96×Na260,to achieve a high prediction accuracy(R2=0.95).The reliability of this prediction was further validated with published data of paddy soils in the same region and other rice regions,demonstrating the regional applicability and prospects of this model.This study underscored the roles of soil properties in Nm characteristics and mechanisms and established a site-specific prediction model based on rapid extractions and edaphic properties of paddy soils,paving the way for developing rapid and precise Nm prediction models.
文摘This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
基金funding enabled and organized by CAUL and its Member Institutions.
文摘The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
文摘BACKGROUND Colorectal polyps(CPs)are important precursor lesions of colorectal cancer,and endoscopic surgery remains the primary treatment option.However,the shortterm recurrence rate post-surgery is high,and the risk factors for recurrence remain unknown.AIM To comprehensively explore risk factors for short-term recurrence of CPs after endoscopic surgery and develop a nomogram prediction model.METHODS Overall,362 patients who underwent endoscopic polypectomy between January 2022 and January 2024 at Nanjing Jiangbei Hospital were included.We screened basic demographic data,clinical and polyp characteristics,surgery-related information,and independent risk factors for CPs recurrence using univariate and multivariate logistic regression analyses.The multivariate analysis results were used to construct a nomogram prediction model,internally validated using Bootstrapping,with performance evaluated using area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS CP re-occurred in 166(45.86%)of the 362 patients within 1 year post-surgery.Multivariate logistic regression analysis showed that age(OR=1.04,P=0.002),alcohol consumption(OR=2.07,P=0.012),Helicobacter pylori infection(OR=2.34,P<0.001),polyp number>2(OR=1.98,P=0.005),sessile polyps(OR=2.10,P=0.006),and adenomatous pathological type(OR=3.02,P<0.001)were independent risk factors for post-surgery recurrence.The nomogram prediction model showed good discriminatory(AUC=0.73)and calibrating power,and decision curve analysis showed that the model had good clinical benefit at risk probabilities>20%.CONCLUSION We identified multiple independent risk factors for short-term recurrence after endoscopic surgery.The nomogram prediction model showed a certain degree of differentiation,calibration,and potential clinical applicability.
基金Supported by Henan Natural Science Foundation,No.232300421181.
文摘BACKGROUND Diagnosis of inflammatory bowel disease and assessment of disease activity are fundamentally reliant on endoscopy.Nonetheless,it is costly and invasive,highlighting the necessity for more accessible and non-invasive biomarkers to assist in the diagnosis and evaluation of inflammatory bowel disease.AIM To examine the correlation of biomarkers with endoscopic activity,evaluate their diagnostic significance,and develop models to forecast endo-scopic activity.METHODS We performed a retrospective single-center analysis of 365 patients with ulcerative colitis(UC),319 with Crohn’s disease(CD)and 100 controls at the First Affiliated Hospital of Zhengzhou University from January 2022 to September 2024.The following biomarkers were analyzed:White blood cell,hemoglobin(Hb),platelet(PLT),neutrophil(N),lymphocyte(L),hematocrit(HCT),eosinophil,albumin(ALB),globulin(GLB),C-reactive protein(CRP),erythrocyte sedimentation rate(ESR),ALB/GLB(AGR),CRP/ALB(CAR),CRP/L(CLR),PLT/ALB(PAR),PLT/L(PLR),and N/L(NLR).RESULTS Serum N,PLT,GLB,CRP,ESR,CAR,CLR,PLR,PAR,and NLR levels were significantly elevated(P<0.001 or P<0.05)in the UC and CD groups compared to controls,whereas Hb,HCT,L,ALB,and AGR were reduced(P<0.001 or P<0.05).Aside from L and eosinophil,substantial differences were observed between mild and severe activity in UC and CD(P<0.001 or P<0.05).UC and CD patients who exhibited an endoscopic response after 14 weeks of treatment had elevated CRP,CAR,and CLR levels at baseline compared to endoscopic nonresponders(P<0.01 or P<0.05).The UC nomogram model utilizing ESR,CAR,and PAR,along with the CD nomogram model employing AGR and PAR,demonstrate predictive significance and clinical applicability for assessing endoscopic activity.CONCLUSION White blood cell,Hb,HCT,PLT,N,CRP,ESR,ALB,GLB,AGR,CAR,CLR,PLR,PAR and NLR are significantly correlated with the endoscopic activity of UC and CD.Patients with UC and CD exhibiting elevated CRP,CAR,and CLR levels are more inclined to respond to treatment.Our nomogram models can precisely forecast endoscopic activity.
文摘The urgent necessity for enhanced risk stratification to improve the efficiency of colonoscopy screening is underscored by the fact that colorectal cancer(CRC)continues to be a primary cause of global cancer mortality.Conventional models mostly rely on generalized obesity markers including body mass index(BMI),which does not effectively represent oncogenic risk linked with abdominal obesity.Liu et al undertook a large-scale case-control study comprising 6484 firsttime colonoscopy patients at a prominent Chinese hospital between 2020 and 2023 to overcome this restriction.Age,male sex,smoking status,and raised waist-hip ratio(WHR)were found by multivariate logistic regression as independent predictors of advanced colorectal neoplasia(ACN).In a validation cohort of 1891 individuals,a new 7-point risk scoring model was created and stratified into low-(5.0%)ACN prevalence,moderate-(10.3%)and high-risk(17.6%).With C-statistic=0.66 the model showed better discriminating ability than the Asia-Pacific Colorectal Screening(APCS)score(C-statistic=0.63)and the BMI-modified APCS model.These results fit newly published data showing central obesity as a major carcinogenic driver via pro-inflammatory visceral adipokine channels.With the use of WHR,patient risk classification is greatly improved,providing a practical tool to make the most of screening resources in the face of rising CRC incidence rates.Finally,multi-ethnic validation is necessary for the WHR-based scoring model to be considered for integration into global CRC preventive frameworks,since it improves the accuracy of ACN risk prediction.
基金Supported by Longyan City Science and Technology Plan Project,No.2024 LYF17067.
文摘BACKGROUND Female depression is a prevalent and increasingly recognized mental health issue.Due to cultural and social factors,many female patients still face challenges in diagnosis and treatment,and traditional assessment methods often fail to identify high-risk individuals accurately.This highlights the necessity of developing more precise predictive tools.Utilizing machine learning(ML)algorithms to construct predictive models may overcome the limitations of traditional methods,providing more comprehensive support for women’s mental health.AIM To construct an ML-nomogram hybrid model that translates multivariate risk predictors of female depressive symptoms into actionable clinical scoring thresholds,optimizing predictive accuracy and interpretability for healthcare applications.METHODS We analyzed data from 7609 female participants aged 18 to 85 years from the Guangdong Provincial Sleep and Psychosomatic Health Survey.Sixteen variables,including anxiety symptoms,insomnia,chronic diseases,exercise habits,and age,were selected based on prior literature and comprehensively incorporated into ML models to maximize predictive information utilization.Three ML algorithms,extreme gradient boosting,support vector machine,and light gradient boosting machine,were employed to construct predictive models.Model performance was evaluated using accuracy,precision,recall,F1 score,and area under the curve(AUC).Feature importance was interpreted using SHapley Additive exPlanations(SHAP),with ablation studies validating the impact of the top five SHAPderived features on predictive performance,and a nomogram was constructed based on these prioritized predictors.Clinical utility was assessed through decision curve analysis.RESULTS The prevalence of depressive symptoms was 6.8%among the sample.The evaluation of predictive models revealed that light gradient boosting machine achieved a top-performing AUC of 0.867,placing it ahead of extreme gradient boosting(AUC=0.862)and support vector machine(AUC=0.849).SHAP analysis identified insomnia,anxiety symptoms,age,chronic disease,and exercise as the top five predictors.The nomogram based on these features demonstrated excellent discrimination(AUC=0.910)and calibration,with significant net benefits in decision curve analysis compared to baseline strategies.The model effectively stratifies depressive symptoms risk,facilitating personalized and quantitative assessments in clinical settings.We also developed an interactive digital version of the nomogram to facilitate its application in clinical practice.CONCLUSION The ML-based model effectively predicts depressive symptoms in women,identifying insomnia,anxiety symptoms,age,chronic diseases,and exercise as key predictors,offering a practical tool for early detection and intervention.
基金supported by the National Key Research and Development Program of China(2022YFB3605902)the National Natural Science Foundation of China(52375411,52293402)。
文摘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.
基金Supported by the Scientific Research Program of Hunan Provincial Health Commission,No.B202313018450.
文摘In this article,we comment on the article published by Yu et al.By employing LASSO regression and Cox proportional hazard models,the article identified nine significant variables affecting survival,including body mass index,Karnofsky performance status,and tumor-node-metastasis staging.We firmly concur with Yu et al regarding the vital significance of clinical prediction models(CPMs),including logistic regression and Cox regression for assessment in esophageal carcinoma(EC).However,the nomogram's limitations and the complexities of integrating genetic factors pose challenges.The integration of immunological data with advanced statistics offers new research directions.High-throughput sequencing and big data,facilitated by machine learning,have revolutionized cancer research but require substantial computational resources.The future of CPMs in EC depends on leveraging these technologies to improve predictive accuracy and clinical application,addressing the need for larger datasets,patientreported outcomes,and regular updates for clinical relevance.
文摘BACKGROUND Few studies have specifically modeled the risk of venous thromboembolism(VTE)for postoperative hepatocellular carcinoma(HCC)patients,although HCC is the third leading cause of cancer death worldwide.This study aimed to develop and validate a nomogram that accurately predicts the risk of VTE in patients after HCC surgery.AIM To develop and validate a nomogram to accurately predict the risk of VTE in postoperative HCC patients by integrating clinical and laboratory risk factors.The model seeks to provide a user-friendly tool for identifying high-risk individuals who may benefit from targeted anticoagulation therapy,thereby improving clinical decision-making and patient outcomes.METHODS Data from patients who underwent HCC surgery at Chongqing University Cancer Hospital in China were analyzed.Through univariate and multivariate logistic regression analyses,independent risk factors for VTE were identified and integrated into a nomogram.The predictive performance of the nomogram was assessed via receiver operating characteristic curves,calibration curves,decision curve analysis and other relevant metrics.RESULTS Of 905 postoperative HCC patients were included in the study.The nomogram incorporated eight independent risk factors for VTE:Karnofsky Performance Scale,base disease,cancer stage(tumor-node-metastasis),chemotherapy,D-dimer concentration,white blood cell count,hemoglobin,and fibrinogen.The C-index for the nomogram model was 0.825 in the training cohort and 0.820 in the validation cohort,indicating good discriminative ability.Calibration plots of the model revealed high concordance between the predicted probabilities and observed outcomes.CONCLUSION We developed and validated a novel nomogram that can accurately estimate the risk of VTE in individual postoperative HCC patients.This model can identify high-risk patients who may benefit from targeted anticoagulation therapy.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2305002Beijing Natural Science Foundation,No.7232079+1 种基金Middle-aged and Young Talent Incubation Programs(Clinical Research)of Beijing Youan Hospital,No.BJYAYY-YN2022-12,No.BJYAYY-YN2022-13,and No.BJYAYY-YN2022-01the China Postdoctoral Science Foundation,No.2023M732410 and No.2024T170595.
文摘BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.
基金National Natural Science Foundation of China (81973749 and 8143594)State Administration of Traditional Chinese Medicine High-level Chinese Medicine Key Discipline Construction Project (zyyzdxk-2023069)。
文摘Objective To develop an onset risk prediction nomogram for patients with homocysteine-type(H-type)hypertension(HTH)based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH.Methods Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shang-hai Hospital of Traditional Chinese Medicine,and Shanghai Hospital of Integrated Tradition-al Chinese and Western Medicine from July 6th 2020 to June 16th 2021,and from August 11th 2023 to January 22nd 2024,were enrolled in this retrospective research.The baselines and clinical biochemical indicators of patients were collected.The SMART-I TCM pulse instru-ment was applied to gather pulse diagram parameters.Multivariate logistic regression was adopted to analyze the risk factors for HTH.RStudio was employed to construct the nomo-gram model,receiver operating characteristic(ROC)curve,and calibration curve(bootstrap self-sampling 200 times),and clinical decision curve were drawn to evaluate the model’s dis-crimination and clinical effectiveness.Results A total of 168 hospitalized patients with essential hypertension were selected and di-vided into non-HTH group(n=29)and HTH group(n=139).Compared with non-HTH group,HTH group had a lower body mass index(BMI),and higher proportions of male pa-tients and drinkers(P<0.05).The ventricular wall thickening(VWT)could not be deter-mined.The proportions of left common carotid intima-media wall thickness(LCCIMWT)and serum creatinine(SCR)were higher in HTH group(P<0.05).The pulse diagram parameter As was significantly higher,and H4/H1 and T1/T were lower in HTH group(P<0.05).Gender,al-cohol consumption,serum creatinine,and the pulse diagram parameter H4/H1 were identi-fied as independent risk factors for HTH(P<0.05).The nomogram’s area under the ROC curve(AUC)was 0.795[95%confidence interval(CI):(0.7066,0.8828)],with a specificity of 0.724 and sensitivity of 0.799.After 200 times repeated bootstrap self-samplings,the calibra-tion curve showed that the simulated curve fits well with the actual curve(x^(2)=9.5002,P=0.3019).The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00.Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.
文摘Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions.These scores aim to provide a structured framework to support clinical judgment.However,their effectiveness varies across patient populations,and their predictive accuracy remains inconsistent.In this review,we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation.While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted,their sensitivity and specificity often fall short in complex clinical settings.Factors such as underlying disease pathophysiology,patient characteristics,and clinician subjectivity impact score performance and reliability.Moreover,disparities in validation across diverse populations limit generalizability.With growing interest in artificial intelligence(AI)and machine learning,there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles.However,current AI approaches face challenges related to interpretability,bias,and ethical implementation.This paper underscores the need for more robust,individualized,and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.
文摘Colorectal cancer(CRC)is a prevalent malignancy,with surgery playing a key role in its treatment.However,perioperative complications,such as anastomotic leaks,infections,and mortality,can significantly affect surgical outcomes,extend hospital stays,and increase healthcare costs.Traditional risk prediction models often lack precision,leading to increased interest in artificial intelligence(AI)for improving risk stratification.This review examines the application of AI,particularly machine learning and deep learning,in predicting perioperative complications in CRC surgery.AI models have been employed to predict a variety of postoperative complications,including readmissions,surgical-site infections,anastomotic leakage,and mortality,by analyzing diverse data sources such as electronic health records,medical imaging,and preoperative markers.Despite the promising results,several challenges remain,including data quality,model generalizability,the complexity of clinical data,and ethical and regulatory concerns.The review emphasizes the need for multicenter,diverse datasets and the integration of AI into clinical workflows to improve model performance and adoption.Future efforts should focus on enhancing the transparency and interpretability of AI models to ensure their successful implementation in clinical practice,ultimately improving patient outcomes and surgical decision-making in CRC surgery.
文摘Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.
文摘Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.
基金Supported by Capital’s Funds for Health Improvement and Research,No.2024-4-4135.
文摘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.
基金Sichuan Province Science and Technology Department Key Research and Development Project,No.2023YFS0473.
文摘In this article,we discuss the study by Cheng et al,published in the World Journal of Gastroenterology,focusing on predictive methods for post-hepatectomy liver failure(PHLF).PHLF is a common and serious complication,and accurate prediction is critical for clinical management.The study examines the potential of ultrasound elastography and splenic size in predicting PHLF.Ultrasound elastography reflects liver functional reserve,while splenic size provides additional predictive value.By integrating these factors with serological markers,we developed a comprehensive prediction model that effectively stratifies patient risk and supports personalized clinical decisions.This approach offers new insights into predicting PHLF.These methods not only assist clinicians in identifying high-risk patients earlier but also provide scientific support for personalized treatment strategies.Future research will aim to validate the model's accuracy with larger sample sizes,further enhancing the clinical application of these non-invasive indicators.
