Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall su...Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.展开更多
Methodological quality(risk of bias)assessment is an important step before study initiation usage.Therefore,accurately judging study type is the first priority,and the choosing proper tool is also important.In this re...Methodological quality(risk of bias)assessment is an important step before study initiation usage.Therefore,accurately judging study type is the first priority,and the choosing proper tool is also important.In this review,we introduced methodological quality assessment tools for randomized controlled trial(including individual and cluster),animal study,non-randomized interventional studies(including follow-up study,controlled before-and-after study,before-after/pre-post study,uncontrolled longitudinal study,interrupted time series study),cohort study,case-control study,cross-sectional study(including analytical and descriptive),observational case series and case reports,comparative effectiveness research,diagnostic study,health economic evaluation,prediction study(including predictor finding study,prediction model impact study,prognostic prediction model study),qualitative study,outcome measurement instruments(including patient-reported outcome measure development,content validity,structural validity,internal consistency,cross-cultural validity/measurement invariance,reliability,measurement error,criterion validity,hypotheses testing for construct validity,and responsiveness),systematic review and meta-analysis,and clinical practice guideline.The readers of our review can distinguish the types of medical studies and choose appropriate tools.In one word,comprehensively mastering relevant knowledge and implementing more practices are basic requirements for correctly assessing the methodological quality.展开更多
Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of tra...Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.展开更多
The paper is concerned with the development and application of the original probability models and supporting them software tools to predict and optimize quality and risks for complex systems. The examples demonstrate...The paper is concerned with the development and application of the original probability models and supporting them software tools to predict and optimize quality and risks for complex systems. The examples demonstrate possibilities to use modeling results from different application spheres and to go in making decision “from a pragmatical filtration of information to generation of the proved ideas and effective decisions”.展开更多
Only a few successful new product development （NPD） projects are continuously supported by the firm when they are expected to assure profitability in the market. The profitability of a project is measured as profit ...Only a few successful new product development （NPD） projects are continuously supported by the firm when they are expected to assure profitability in the market. The profitability of a project is measured as profit ratio （PR）, the profit is divided by the cost. The profit figure is changed depending on either internal risks or uncertainties occurring externally. More risks require higher response costs to them and uncontrollable uncertainties affect NPD projects either positively or negatively. In this study, a PR model is developed to predict the profitability of a project at a given time. The model minimizes the response cost computed under two extreme response strategies, such as ＂Avoid＂ and ＂Acceptance＂ for the internal threats. Also, the model reflects the sales volume changes due to external uncertainties. The linear programming （LP） method determines the optimal probability of the response strategy under three scenarios of defining the relationship between risk avoidance and risk acceptance. It can be utilized to make a GO/NOGO decision on the project based on the prediction results at any gate of the NPD process. The solving procedure is provided to apply the developed model for real cases.展开更多
This study examines how socio-economic characteristics predict flood risk in London,England,using machine learning algorithms.The socio-economic variables considered included race,employment,crime and poverty measures...This study examines how socio-economic characteristics predict flood risk in London,England,using machine learning algorithms.The socio-economic variables considered included race,employment,crime and poverty measures.A stacked generalization(SG)model combines randomforest(RF),support vector machine(SVM),and XGBoost.Binary classification issues employ RF as the basis model and SVM as the meta-model.In multiclass classification problems,RF and SVM are base models while XGBoost is meta-model.The study utilizes flood risk labels for London areas and census data to train these models.This study found that SVM performs well in binary classifications with an accuracy rate of 0.60 and an area under the curve of 0.62.XGBoost outperforms other multiclass classification methods with 0.62 accuracy.Multiclass algorithms may perform similarly to binary classification jobs due to reduced data complexity when combining classes.The statistical significance of the result underscores their robustness,respectively.The findings reveal a significant correlation between flood risk and socio-economic factors,emphasizing the importance of these variables in predicting flood susceptibility.These results have important implications for disaster relief management and future research should focus on refining these models to improve predictive accuracy and exploring socio-economic factors.展开更多
Since 1972 Rita typhoon attacked on Dalian Port and induced severe catastrophe, we were studied on statistical prediction model of typhoon induced wave height and wind speed. With an increasing tendency of the natural...Since 1972 Rita typhoon attacked on Dalian Port and induced severe catastrophe, we were studied on statistical prediction model of typhoon induced wave height and wind speed. With an increasing tendency of the natural hazards frequency and intensity, risk assessment of some design codes for coastal defence infrastructures should be of paramount importance influencing the economic development and a lot of lifes in China. Comparison between existing extreme statistical model like Gumbel, Weibull, P-III distribution or Probable Maximum Typhoon/Hurricane (PMT/PMH), Design Basis Flood (DBF) with our 1975-1980 proposed (CEVD) model showed that all the planned, designed and constructed coastal infrastructures accepted the traditional safety regulations are menaced by possibility of future ty-phoon/hurricane disasters and cannot satisfy the safety requirements with the increasing tendency of the extreme natural hazards. Our first publication in US (J. of Waterway Port Coastal & Ocean Eng. ASCE, 1980, ww4) proposed an new model “Compound Extreme Value Distribution” used for China sea, after then the model was used in “Long term Distribution of Hurricane Characteristics” for Gulf of Mexico & Atlantic coasts, U.S. (OTC.1982). 2005 hurricane Katrina, Rita and 2012 hurricane Sandy induced disasters proved 1982 CEVD and CEVD has been developed into Multivariate Compound Extreme Value Distribution (MCEVD). 2006 MCEVD predicted extreme hazards in New Orleans, Gulf of Mexico and Philadelphia areas. 2013 typhoon Fitow induced disaster in China also proved MCEVD 2006 predicted results.展开更多
Flooding has been one of the recurring occurred natural disasters that induce detrimental impacts on humans, property and environment. Frequent floods is a severe issue and a complex natural phenomenon in Pakistan wit...Flooding has been one of the recurring occurred natural disasters that induce detrimental impacts on humans, property and environment. Frequent floods is a severe issue and a complex natural phenomenon in Pakistan with respect to population affected, environmental degradations, and socio-economic and property damages. The Super Flood, which hit Sindh in 2010, has turned out to be a wakeup call and has underlined the overwhelming challenge of natural calamities, as 2010 flood and the preceding flood in 2011 caused a huge loss to life, property and land use. These floods resulted in disruption of power, telecommunication, and water utilities in many districts of Pakistan, including 22 districts of Sindh. These floods call for risk assessment and hazard mapping of Lower Indus Basin flowing in the Sindh Province as such areas were also inundated in 2010 flood, which were not flooded in the past in this manner. This primary focus of this paper is the use of Multi-criteria Evaluation (MCE) methods in integration with the Geographical Information System (GIS) for the analysis of areas prone to flood. This research demonstrated how GIS tools can be used to produce map of flood vulnerable areas using MCE techniques. Slope, Aspect, Curvature, Soil, and Distance from Drainage, Land use, Precipitation, Flow Direction, and Flow Accumulation are taken as the causative factors for flooding in Lower Indus Basin. Analytical Hierarchy Process-AHP was used for the calculation of weights of all these factors. Finally, a flood hazard Map of Lower Indus Basin was generated which delineates the flood prone areas in the Sindh province along Indus River Basin that could be inundated by potential flooding in future. It is aimed that flood hazard mapping and risk assessment using open source geographic information system can serve as a handy tool for the development of land-use strategies so as to decrease the impact from flooding.展开更多
目的:系统评价心脏植入式电子设备(CIED)植入术后设备感染(DRI)的风险预测模型。方法:通过计算机检索PubMed、Embase、Web of Science、Cochrane图书馆、CINAHL、中国生物医学文献数据库、中国知网、维普网、万方数据库中与CIED植入术后...目的:系统评价心脏植入式电子设备(CIED)植入术后设备感染(DRI)的风险预测模型。方法:通过计算机检索PubMed、Embase、Web of Science、Cochrane图书馆、CINAHL、中国生物医学文献数据库、中国知网、维普网、万方数据库中与CIED植入术后DRI风险预测模型相关的文献,检索时间为从建库至2023年12月2日。由2名研究者独立筛选文献、提取资料并完成纳入文献的偏倚风险与适用性评价。结果:共纳入16项研究,模型总体适用性较好,但偏倚风险较高,ROC曲线的AUC为0.67~0.96。11项研究完成了内部验证,5项研究进行了外部验证。囊袋和(或)电极重置/装置升级、肾功能不全或肾功能衰竭、年龄、植入埋藏式心脏复律除颤器或心脏再同步化治疗、使用抗凝药是DRI的预测因子。结论:目前CIED植入术后DRI风险预测模型整体性能较好,适用性较好,但偏倚风险较高。需在数据来源、变量筛选、模型评价等方面提高研究质量,开展前瞻性队列研究,完善现有模型的外部验证,并积极研发适用于我国人群的预测模型。展开更多
基金This workwas supported by the Medical and Health Science and Technology Project of Zhejiang Province(No.2021KY180).
文摘Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.
基金supported(in part)by the Entrusted Project of National commission on health and health of China(No.2019099)the National Key Research and Development Plan of China(2016YFC0106300)the Nature Science Foundation of Hubei Province(2019FFB03902)。
文摘Methodological quality(risk of bias)assessment is an important step before study initiation usage.Therefore,accurately judging study type is the first priority,and the choosing proper tool is also important.In this review,we introduced methodological quality assessment tools for randomized controlled trial(including individual and cluster),animal study,non-randomized interventional studies(including follow-up study,controlled before-and-after study,before-after/pre-post study,uncontrolled longitudinal study,interrupted time series study),cohort study,case-control study,cross-sectional study(including analytical and descriptive),observational case series and case reports,comparative effectiveness research,diagnostic study,health economic evaluation,prediction study(including predictor finding study,prediction model impact study,prognostic prediction model study),qualitative study,outcome measurement instruments(including patient-reported outcome measure development,content validity,structural validity,internal consistency,cross-cultural validity/measurement invariance,reliability,measurement error,criterion validity,hypotheses testing for construct validity,and responsiveness),systematic review and meta-analysis,and clinical practice guideline.The readers of our review can distinguish the types of medical studies and choose appropriate tools.In one word,comprehensively mastering relevant knowledge and implementing more practices are basic requirements for correctly assessing the methodological quality.
文摘Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.
文摘The paper is concerned with the development and application of the original probability models and supporting them software tools to predict and optimize quality and risks for complex systems. The examples demonstrate possibilities to use modeling results from different application spheres and to go in making decision “from a pragmatical filtration of information to generation of the proved ideas and effective decisions”.
文摘Only a few successful new product development （NPD） projects are continuously supported by the firm when they are expected to assure profitability in the market. The profitability of a project is measured as profit ratio （PR）, the profit is divided by the cost. The profit figure is changed depending on either internal risks or uncertainties occurring externally. More risks require higher response costs to them and uncontrollable uncertainties affect NPD projects either positively or negatively. In this study, a PR model is developed to predict the profitability of a project at a given time. The model minimizes the response cost computed under two extreme response strategies, such as ＂Avoid＂ and ＂Acceptance＂ for the internal threats. Also, the model reflects the sales volume changes due to external uncertainties. The linear programming （LP） method determines the optimal probability of the response strategy under three scenarios of defining the relationship between risk avoidance and risk acceptance. It can be utilized to make a GO/NOGO decision on the project based on the prediction results at any gate of the NPD process. The solving procedure is provided to apply the developed model for real cases.
文摘This study examines how socio-economic characteristics predict flood risk in London,England,using machine learning algorithms.The socio-economic variables considered included race,employment,crime and poverty measures.A stacked generalization(SG)model combines randomforest(RF),support vector machine(SVM),and XGBoost.Binary classification issues employ RF as the basis model and SVM as the meta-model.In multiclass classification problems,RF and SVM are base models while XGBoost is meta-model.The study utilizes flood risk labels for London areas and census data to train these models.This study found that SVM performs well in binary classifications with an accuracy rate of 0.60 and an area under the curve of 0.62.XGBoost outperforms other multiclass classification methods with 0.62 accuracy.Multiclass algorithms may perform similarly to binary classification jobs due to reduced data complexity when combining classes.The statistical significance of the result underscores their robustness,respectively.The findings reveal a significant correlation between flood risk and socio-economic factors,emphasizing the importance of these variables in predicting flood susceptibility.These results have important implications for disaster relief management and future research should focus on refining these models to improve predictive accuracy and exploring socio-economic factors.
文摘Since 1972 Rita typhoon attacked on Dalian Port and induced severe catastrophe, we were studied on statistical prediction model of typhoon induced wave height and wind speed. With an increasing tendency of the natural hazards frequency and intensity, risk assessment of some design codes for coastal defence infrastructures should be of paramount importance influencing the economic development and a lot of lifes in China. Comparison between existing extreme statistical model like Gumbel, Weibull, P-III distribution or Probable Maximum Typhoon/Hurricane (PMT/PMH), Design Basis Flood (DBF) with our 1975-1980 proposed (CEVD) model showed that all the planned, designed and constructed coastal infrastructures accepted the traditional safety regulations are menaced by possibility of future ty-phoon/hurricane disasters and cannot satisfy the safety requirements with the increasing tendency of the extreme natural hazards. Our first publication in US (J. of Waterway Port Coastal & Ocean Eng. ASCE, 1980, ww4) proposed an new model “Compound Extreme Value Distribution” used for China sea, after then the model was used in “Long term Distribution of Hurricane Characteristics” for Gulf of Mexico & Atlantic coasts, U.S. (OTC.1982). 2005 hurricane Katrina, Rita and 2012 hurricane Sandy induced disasters proved 1982 CEVD and CEVD has been developed into Multivariate Compound Extreme Value Distribution (MCEVD). 2006 MCEVD predicted extreme hazards in New Orleans, Gulf of Mexico and Philadelphia areas. 2013 typhoon Fitow induced disaster in China also proved MCEVD 2006 predicted results.
文摘Flooding has been one of the recurring occurred natural disasters that induce detrimental impacts on humans, property and environment. Frequent floods is a severe issue and a complex natural phenomenon in Pakistan with respect to population affected, environmental degradations, and socio-economic and property damages. The Super Flood, which hit Sindh in 2010, has turned out to be a wakeup call and has underlined the overwhelming challenge of natural calamities, as 2010 flood and the preceding flood in 2011 caused a huge loss to life, property and land use. These floods resulted in disruption of power, telecommunication, and water utilities in many districts of Pakistan, including 22 districts of Sindh. These floods call for risk assessment and hazard mapping of Lower Indus Basin flowing in the Sindh Province as such areas were also inundated in 2010 flood, which were not flooded in the past in this manner. This primary focus of this paper is the use of Multi-criteria Evaluation (MCE) methods in integration with the Geographical Information System (GIS) for the analysis of areas prone to flood. This research demonstrated how GIS tools can be used to produce map of flood vulnerable areas using MCE techniques. Slope, Aspect, Curvature, Soil, and Distance from Drainage, Land use, Precipitation, Flow Direction, and Flow Accumulation are taken as the causative factors for flooding in Lower Indus Basin. Analytical Hierarchy Process-AHP was used for the calculation of weights of all these factors. Finally, a flood hazard Map of Lower Indus Basin was generated which delineates the flood prone areas in the Sindh province along Indus River Basin that could be inundated by potential flooding in future. It is aimed that flood hazard mapping and risk assessment using open source geographic information system can serve as a handy tool for the development of land-use strategies so as to decrease the impact from flooding.
文摘目的:系统评价心脏植入式电子设备(CIED)植入术后设备感染(DRI)的风险预测模型。方法:通过计算机检索PubMed、Embase、Web of Science、Cochrane图书馆、CINAHL、中国生物医学文献数据库、中国知网、维普网、万方数据库中与CIED植入术后DRI风险预测模型相关的文献,检索时间为从建库至2023年12月2日。由2名研究者独立筛选文献、提取资料并完成纳入文献的偏倚风险与适用性评价。结果:共纳入16项研究,模型总体适用性较好,但偏倚风险较高,ROC曲线的AUC为0.67~0.96。11项研究完成了内部验证,5项研究进行了外部验证。囊袋和(或)电极重置/装置升级、肾功能不全或肾功能衰竭、年龄、植入埋藏式心脏复律除颤器或心脏再同步化治疗、使用抗凝药是DRI的预测因子。结论:目前CIED植入术后DRI风险预测模型整体性能较好,适用性较好,但偏倚风险较高。需在数据来源、变量筛选、模型评价等方面提高研究质量,开展前瞻性队列研究,完善现有模型的外部验证,并积极研发适用于我国人群的预测模型。
