期刊文献+
共找到7,048篇文章
< 1 2 250 >
每页显示 20 50 100
Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China
1
作者 MA Jie CHU Lijuan +3 位作者 SUN Jing WANG Shenglan GE Miao DENG Li 《Journal of Mountain Science》 SCIE CSCD 2024年第1期100-112,共13页
This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed ... This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors. 展开更多
关键词 Monte Carlo simulation Health risk assessment Trace metal(loid)s Positive matrix factorization Agricultural soils
下载PDF
Risk factors and risk prediction model for mucocutaneous separation in enterostomy patients:A single center experience
2
作者 Yun Liu Hong Li +1 位作者 Jin-Jing Wu Jian-Hong Ye 《World Journal of Clinical Cases》 SCIE 2024年第33期6620-6628,共9页
BACKGROUND Mucocutaneous separation(MCS)is a common postoperative complication in enterostomy patients,potentially leading to significant morbidity.Early identification of risk factors is crucial for preventing this c... BACKGROUND Mucocutaneous separation(MCS)is a common postoperative complication in enterostomy patients,potentially leading to significant morbidity.Early identification of risk factors is crucial for preventing this condition.However,predictive models for MCS remain underdeveloped.AIM To construct a risk prediction model for MCS in enterostomy patients and assess its clinical predictive accuracy.METHODS A total of 492 patients who underwent enterostomy from January 2019 to March 2023 were included in the study.Patients were divided into two groups,the MCS group(n=110),and the non-MCS(n=382)based on the occurrence of MCS within the first 3 weeks after surgery.Univariate and multivariate analyses were used to identify the independent predictive factors of MCS and the model constructed.Receiver operating characteristic curve analysis was used to assess the model’s performance.RESULTS The postoperative MCS incidence rate was 22.4%.Suture dislodgement(P<0.0001),serum albumin level(P<0.0001),body mass index(BMI)(P=0.0006),hemoglobin level(P=0.0409),intestinal rapture(P=0.0043),incision infection(P<0.0001),neoadjuvant therapy(P=0.0432),stoma site(P=0.0028)and elevated intra-abdominal pressure(P=0.0395)were potential predictive factors of MCS.Suture dislodgement[P<0.0001,OR:28.007595%CI:(11.0901-82.1751)],serum albumin level(P=0.0008,OR:0.3504,95%CI:[0.1902-0.6485]),BMI[P=0.0045,OR:2.1361,95%CI:(1.2660-3.6235)],hemoglobin level[P=0.0269,OR:0.5164,95%CI:(0.2881-0.9324)],intestinal rapture[P=0.0351,OR:3.0694,95%CI:(1.0482-8.5558)],incision infection[P=0.0179,OR:0.2885,95%CI:(0.0950-0.7624)]and neoadjuvant therapy[P=0.0112,OR:1.9769,95%CI:(1.1718-3.3690)]were independent predictive factors and included in the model.The model had an area under the curve of 0.827 and good clinical utility on decision curve analysis.CONCLUSION The mucocutaneous separation prediction model constructed in this study has good predictive performance and can provide a reference for early warning of mucocutaneous separation in enterostomy patients. 展开更多
关键词 ENTEROSTOMY Mucocutaneous separation risk assessment model Performance validation
下载PDF
Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke
3
作者 Xiao-Chen Liu Xiao-Jie Chang +4 位作者 Si-Ren Zhao Shan-Shan Zhu Yan-Yan Tian Jing Zhang Xin-Yue Li 《World Journal of Clinical Cases》 SCIE 2024年第20期4048-4056,共9页
BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection... BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy. 展开更多
关键词 Acute ischemic stroke INFECTION risk factors Nomogram prediction model Chronic obstructive pulmonary disease
下载PDF
Risk factors and survival prediction model establishment for prognosis in patients with radical resection of gallbladder cancer
4
作者 Xing-Fei Li Tan-Tu Ma Tao Li 《World Journal of Gastrointestinal Surgery》 SCIE 2024年第10期3239-3252,共14页
BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains th... BACKGROUND Gallbladder cancer(GBC)is the most common malignant tumor of the biliary system,and is often undetected until advanced stages,making curative surgery unfeasible for many patients.Curative surgery remains the only option for long-term survival.Accurate postsurgical prognosis is crucial for effective treatment planning.tumor-node-metastasis staging,which focuses on tumor infiltration,lymph node metastasis,and distant metastasis,limits the accuracy of prognosis.Nomograms offer a more comprehensive and personalized approach by visually analyzing a broader range of prognostic factors,enhancing the precision of treatment planning for patients with GBC.AIM A retrospective study analyzed the clinical and pathological data of 93 patients who underwent radical surgery for GBC at Peking University People's Hospital from January 2015 to December 2020.Kaplan-Meier analysis was used to calculate the 1-,2-and 3-year survival rates.The log-rank test was used to evaluate factors impacting prognosis,with survival curves plotted for significant variables.Single-factor analysis revealed statistically significant differences,and multivariate Cox regression identified independent prognostic factors.A nomogram was developed and validated with receiver operating characteristic curves and calibration curves.Among 93 patients who underwent radical surgery for GBC,30 patients survived,accounting for 32.26%of the sample,with a median survival time of 38 months.The 1-year,2-year,and 3-year survival rates were 83.87%,68.82%,and 53.57%,respectively.Univariate analysis revealed that carbohydrate antigen 19-9 expre-ssion,T stage,lymph node metastasis,histological differentiation,surgical margins,and invasion of the liver,ex-trahepatic bile duct,nerves,and vessels(P≤0.001)significantly impacted patient prognosis after curative surgery.Multivariate Cox regression identified lymph node metastasis(P=0.03),histological differentiation(P<0.05),nerve invasion(P=0.036),and extrahepatic bile duct invasion(P=0.014)as independent risk factors.A nomogram model with a concordance index of 0.838 was developed.Internal validation confirmed the model's consistency in predicting the 1-year,2-year,and 3-year survival rates.CONCLUSION Lymph node metastasis,tumor differentiation,extrahepatic bile duct invasion,and perineural invasion are independent risk factors.A nomogram based on these factors can be used to personalize and improve treatment strategies. 展开更多
关键词 Gallbladder cancer radical surgery Prognosis of gallbladder cancer Multifactor analysis Independent risk factors NOMOGRAM Survival prediction model
下载PDF
Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease 被引量:1
5
作者 Cheng Wang Haoran Zhu Congjun Rao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第1期691-718,共28页
Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In thi... Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results. 展开更多
关键词 CVD influencing factors risk assessment machine learning two-stage model
下载PDF
Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model 被引量:1
6
作者 Rui-Xiao Li Xue-Lian Li +4 位作者 Guo-Jun Wu Yong-Hua Lei Xiao-Shun Li Bo Li Jian-Xin Ni 《World Journal of Psychiatry》 SCIE 2024年第2期255-265,共11页
BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages ... BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions. 展开更多
关键词 Prostate cancer CASTRATION Anxiety and depression risk factors risk prediction model
下载PDF
A risk assessment method considering risk attributes and work safety informational needs and its application
7
作者 Cong Luo Yunsheng Zhao Ke Xu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第4期253-262,共10页
The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evo... The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility. 展开更多
关键词 risk assessment Safey “51X”evaluation indicator system Four-tier risk assessment model risk attributes Process system
下载PDF
Construction and validation of a risk-prediction model for anastomotic leakage after radical gastrectomy: A cohort study in China
8
作者 Jinrui Wang Xiaolin Liu +6 位作者 Hongying Pan Yihong Xu Mizhi Wu Xiuping Li Yang Gao Meijuan Wang Mengya Yan 《Laparoscopic, Endoscopic and Robotic Surgery》 2024年第1期34-43,共10页
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
关键词 Stomach neoplasms Anastomotic leak risk factors Prediction model risk assessment
下载PDF
Risk factors for lymph node metastasis in superficial esophageal squamous cell carcinoma
9
作者 Yan-Bo Yu 《World Journal of Gastroenterology》 SCIE CAS 2024年第13期1810-1814,共5页
In this editorial,we comment on the article by Wang et al published in the recent issue of the World Journal of Gastroenterology in 2023.We focused on identifying risk factors for lymph node metastasis(LNM)in superfic... In this editorial,we comment on the article by Wang et al published in the recent issue of the World Journal of Gastroenterology in 2023.We focused on identifying risk factors for lymph node metastasis(LNM)in superficial esophageal squamous cell carcinoma(SESCC)patients and how to construct a simple and reliable clinical prediction model to assess the risk of LNM in SESCC patients,thereby helping to guide the selection of an appropriate treatment plan.The current standard treatment for SESCC is radical esophagectomy with lymph node dissection.However,esophagectomy is associated with considerable morbidity and mortality.Endoscopic resection(ER)offers a safer and less invasive alternative to surgical resection and can enable the patient's quality of life to be maintained while providing a satisfactory outcome.However,since ER is a localized treatment that does not allow for lymph node dissection,the risk of LNM in SESCC limits the effectiveness of ER.Understanding LNM status can aid in determining whether patients with SESCC can be cured by ER without the need for additional esophagectomy.Previous studies have shown that tumor size,macroscopic type of tumor,degree of differentiation,depth of tumor invasion,and lymphovascular invasion are factors associated with LNM in patients with SESCC.In addition,tumor budding is commonly associated with LNM,recurrence,and distant metastasis,but this topic has been less covered in previous studies.By comprehensively evaluating the above risk factors for LNM,useful evidence can be obtained for doctors to select appropriate treatments for SESCC patients. 展开更多
关键词 Superficial esophageal squamous cell carcinoma Endoscopic resection Lymph node metastasis risk factors Tumor budding Predictive model
下载PDF
Clinical risk factors for preterm birth and evaluating maternal psychology in the postpartum period
10
作者 Jia-Jun Chen Xue-Jin Chen +2 位作者 Qiu-Min She Jie-Xi Li Qiu-Hong Luo 《World Journal of Psychiatry》 SCIE 2024年第5期661-669,共9页
BACKGROUND Although the specific pathogenesis of preterm birth(PTB)has not been thoroughly clarified,it is known to be related to various factors,such as pregnancy complications,maternal socioeconomic factors,lifestyl... BACKGROUND Although the specific pathogenesis of preterm birth(PTB)has not been thoroughly clarified,it is known to be related to various factors,such as pregnancy complications,maternal socioeconomic factors,lifestyle habits,reproductive history,environmental and psychological factors,prenatal care,and nutritional status.PTB has serious implications for newborns and families and is associated with high mortality and complications.Therefore,the prediction of PTB risk can facilitate early intervention and reduce its resultant adverse consequences.AIM To analyze the risk factors for PTB to establish a PTB risk prediction model and to assess postpartum anxiety and depression in mothers.METHODS A retrospective analysis of 648 consecutive parturients who delivered at Shenzhen Bao’an District Songgang People’s Hospital between January 2019 and January 2022 was performed.According to the diagnostic criteria for premature infants,the parturients were divided into a PTB group(n=60)and a full-term(FT)group(n=588).Puerperae were assessed by the Self-rating Anxiety Scale(SAS)and Self rating Depression Scale(SDS),based on which the mothers with anxiety and depression symptoms were screened for further analysis.The factors affecting PTB were analyzed by univariate analysis,and the related risk factors were identified by logistic regression.RESULTS According to univariate analysis,the PTB group was older than the FT group,with a smaller weight change and greater proportions of women who underwent artificial insemination and had gestational diabetes mellitus(P<0.05).In addition,greater proportions of women with reproductive tract infections and greater white blood cell(WBC)counts(P<0.05),shorter cervical lengths in the second trimester and lower neutrophil percentages(P<0.001)were detected in the PTB group than in the FT group.The PTB group exhibited higher postpartum SAS and SDS scores than did the FT group(P<0.0001),with a higher number of mothers experiencing anxiety and depression(P<0.001).Multivariate logistic regression analysis revealed that a greater maternal weight change,the presence of gestational diabetes mellitus,a shorter cervical length in the second trimester,a greater WBC count,and the presence of maternal anxiety and depression were risk factors for PTB(P<0.01).Moreover,the risk score of the FT group was lower than that of the PTB group,and the area under the curve of the risk score for predicting PTB was greater than 0.9.CONCLUSION This study highlights the complex interplay between postpartum anxiety and PTB,where maternal anxiety may be a potential risk factor for PTB,with PTB potentially increasing the incidence of postpartum anxiety in mothers.In addition,a greater maternal weight change,the presence of gestational diabetes mellitus,a shorter cervical length,a greater WBC count,and postpartum anxiety and depression were identified as risk factors for PTB. 展开更多
关键词 Preterm birth risk factors Postpartum psychological state risk model Prediction
下载PDF
Analysis of risk factors of suicidal ideation in adolescent patients with depression and construction of prediction model
11
作者 Jun-Chao Zhou Yan Cao +1 位作者 Xu-Yuan Xu Zhen-Ping Xian 《World Journal of Psychiatry》 SCIE 2024年第3期388-397,共10页
BACKGROUND Major depressive disorder is a common mental illness among adolescents and is the largest disease burden in this age group.Most adolescent patients with depression have suicidal ideation(SI);however,few stu... BACKGROUND Major depressive disorder is a common mental illness among adolescents and is the largest disease burden in this age group.Most adolescent patients with depression have suicidal ideation(SI);however,few studies have focused on the factors related to SI,and effective predictive models are lacking.AIM To construct a risk prediction model for SI in adolescent depression and provide a reference assessment tool for prevention.METHODS The data of 150 adolescent patients with depression at the First People's Hospital of Lianyungang from June 2020 to December 2022 were retrospectively analyzed.Based on whether or not they had SI,they were divided into a SI group(n=91)and a non-SI group(n=59).The general data and laboratory indices of the two groups were compared.Logistic regression was used to analyze the factors influencing SI in adolescent patients with depression,a nomogram prediction model was constructed based on the analysis results,and internal evaluation was performed.Receiver operating characteristic and calibration curves were used to evaluate the model’s efficacy,and the clinical application value was evaluated using decision curve analysis(DCA).RESULTS There were differences in trauma history,triggers,serum ferritin levels(SF),highsensitivity C-reactive protein levels(hs-CRP),and high-density lipoprotein(HDLC)levels between the two groups(P<0.05).Logistic regression analysis showed that trauma history,predisposing factors,SF,hs-CRP,and HDL-C were factors influencing SI in adolescent patients with depression.The area under the curve of the nomogram prediction model was 0.831(95%CI:0.763–0.899),sensitivity was 0.912,and specificity was 0.678.The higher net benefit of the DCA and the average absolute error of the calibration curve were 0.043,indicating that the model had a good fit.CONCLUSION The nomogram prediction model based on trauma history,triggers,ferritin,serum hs-CRP,and HDL-C levels can effectively predict the risk of SI in adolescent patients with depression. 展开更多
关键词 Adolescents DEPRESSION Suicidal ideation risk factors Prediction model FERRITIN
下载PDF
Risk assessment of high-speed railway CTC system based on improved game theory and cloud model
12
作者 Yanhao Sun Tao Zhang +2 位作者 Shuxin Ding Zhiming Yuan Shengliang Yang 《Railway Sciences》 2024年第3期388-410,共23页
Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable c... Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system. 展开更多
关键词 High-speed railway Centralized traffic control risk assessment Game theory Cloud model Paper type Research paper
下载PDF
Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study
13
作者 Shadman Mahmood Khan Pathan Sakan Binte Imran 《Journal of Intelligent Learning Systems and Applications》 2024年第1期12-22,共11页
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
关键词 Cardiovascular Disease Machine Learning Deep Learning Predictive modeling risk assessment Comparative Analysis Gradient Boosting LSTM
下载PDF
Risk factors and prediction model for inpatient surgical site infection after elective abdominal surgery 被引量:1
14
作者 Jin Zhang Fei Xue +8 位作者 Si-Da Liu Dong Liu Yun-Hua Wu Dan Zhao Zhou-Ming Liu Wen-Xing Ma Ruo-Lin Han Liang Shan Xiang-Long Duan 《World Journal of Gastrointestinal Surgery》 SCIE 2023年第3期387-397,共11页
BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challengin... BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.AIM To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.METHODS We retrospectively analysed the inpatient records of Shaanxi Provincial People’s Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002(NRS 2002)scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance(NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.RESULTS A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus(42.2%), the liver(27.6%), the gastrointestinal tract(19.1%), the appendix(5.9%), the kidney(3.7%), and the groin area(1.4%). SSI occurred in 5% of the patients(n = 150). The risk factors associated with SSI were as follows: Age;gender;marital status;place of residence;history of diabetes;surgical season;surgical site;NRS 2002 score;preoperative white blood cell, procalcitonin(PCT), albumin, and low-density lipoprotein cholesterol(LDL) levels;preoperative antibiotic use;anaesthesia method;incision grade;NNIS score;intraoperative blood loss;intraoperative drainage tube placement;surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio(OR) = 5.698, 95% confidence interval(CI): 3.305-9.825, P = 0.001], antibiotic use(OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3(OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia(OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2(OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L(OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L(OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL(OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season(P < 0.05), surgical site(P < 0.05), and incision grade I or Ⅲ(P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score(0.662).CONCLUSION The patient’s condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery. 展开更多
关键词 Surgical site infections risk factors Abdominal surgery Prediction model
下载PDF
A reliability-oriented genetic algorithm-levenberg marquardt model for leak risk assessment based on time-frequency features
15
作者 Ying-Ying Wang Hai-Bo Sun +4 位作者 Jin Yang Shi-De Wu Wen-Ming Wang Yu-Qi Li Ze-Qing Lin 《Petroleum Science》 SCIE EI CSCD 2023年第5期3194-3209,共16页
Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected in... Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines. 展开更多
关键词 Leak risk assessment Oil pipeline GA-LM model Data derivation Time-frequency features
下载PDF
A meta-analysis of risk factors for epilepsy after acute ischaemic stroke and the development of a predictive model
16
作者 YANG Yi-hao CHEN Shi-hui +4 位作者 LI Zong-jun JIA Dan-dan ZOU Qin Cai Yi LI Qi-fu 《Journal of Hainan Medical University》 CAS 2023年第11期37-47,共11页
Objective:To screen risk factors for epilepsy after acute ischaemic stroke based on meta-analysis and cohort study and to establish a predictive model.Methods:Computer searches of MEDLINE,Embase,Cochrane library,Web o... Objective:To screen risk factors for epilepsy after acute ischaemic stroke based on meta-analysis and cohort study and to establish a predictive model.Methods:Computer searches of MEDLINE,Embase,Cochrane library,Web of Scinence,PubMed,CNKI,and WanFang Data data were conducted to collect literature on epilepsy after in acute ischemic stroke,from database creation to September 1,2022.The RRs and their 95%confidence intervals(CI)for risk factors for post stroke epilepsy were extracted for each study,and pooled estimates of the RRs and 95%CIs for each study were generated using either a random-effects model or a fixed-effects model.Beta coefficients for each risk factor were calculated based on the combined RR and their corresponding 95%CIs.The beta coefficients were multiplied by 10 and rounded.Results:Ten articles were identified for final inclusion in this meta-analysis,with a total of 141948 cases and 3702 cases of post stroke epilepsy.The risk factors included in the final risk prediction model were infarct size(RR 4.67,95%CI 1.41~15.47;P=0.01),stroke recuRRence(RR 2.48,95%CI 2.01~3.05;P<0.00001),stroke etiology(RR 1.70,95%CI 1.34~2.15;P<0.00001),stroke severity(RR 1.70,95%CI 1.34~2.15;P<0.00001),and stroke risk.stroke severity(RR 1.53,95%CI 1.39~1.70;P<0.00001),NIHSS score(RR 2.91,95%CI 1.64~5.61;P=0.0003),early-onset epilepsy(RR 5.62,95%CI 5.08~6.22;P<0.00001),cortical lesions(RR 3.83.95%CI 2.23~6.58;P<0.00001),total anterior circulation infarction(RR 18.94,95%CI 10.38~34.57;P<0.00001),partial anterior circulation infarction(RR 4.39,95%CI 2.29~8.40;P<0.00001),cardiovascular events(RR 1.78,95%CI 1.59~1.99;P<0.00001).Conclusion:Based on a systematic review and meta-analysis,we developed a simple risk prediction model for late epilepsy in baseline ischemic stroke that integrates clinical risk factors,including infarct size,stroke recurrence,stroke etiology,stroke severity,NIHSS score,early onset epilepsy,cortical lesions,stroke subtype,and cardiovascular events. 展开更多
关键词 Post stroke epilepsy risk factors Predictive model Acute ischaemic stroke
下载PDF
Risk Assessment of Deep-Water Horizontal X-Tree Installation 被引量:1
17
作者 MENG Wen-bo FU Guang-ming +3 位作者 HUANG Yi LIU Shu-jie HUANG Liang GAOYong-hai 《China Ocean Engineering》 SCIE EI CSCD 2024年第2期210-220,共11页
Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a ... Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a modular risk evaluation model based on a fuzzy fault tree.First,through the analysis of the main process oftree down and combining the Offshore&Onshore Reliability Data(OREDA)failure statistics and the operation procedure and the data provided by the job,the fault tree model of risk analysis of the tree down installation was established.Then,by introducing the natural language of expert comprehensive evaluation and combining fuzzy principles,quantitative analysis was carried out,and the fuzzy number was used to calculate the failure probability of a basic event and the occurrence probability of a top event.Finally,through a sensitivity analysis of basic events,the basic events of top events significantly affected were determined,and risk control and prevention measures for the corresponding high-risk factors were proposed for subsea horizontal X-tree down installation. 展开更多
关键词 subsea horizontal X-tree risk assessment fuzzy fault tree modular risk evaluation model
下载PDF
Risk of cardiovascular death in patients with hepatocellular carcinoma based on the Fine-Gray model
18
作者 Yu-Liang Zhang Zi-Rong Liu +5 位作者 Zhi Liu Yi Bai Hao Chi Da-Peng Chen Ya-Min Zhang Zi-Lin Cui 《World Journal of Gastrointestinal Oncology》 SCIE 2024年第3期844-856,共13页
BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common types of cancers worldwide,ranking fifth among men and seventh among women,resulting in more than 7 million deaths annually.With the development of med... BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common types of cancers worldwide,ranking fifth among men and seventh among women,resulting in more than 7 million deaths annually.With the development of medical tech-nology,the 5-year survival rate of HCC patients can be increased to 70%.How-ever,HCC patients are often at increased risk of cardiovascular disease(CVD)death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients.Moreover,CVD and cancer have become major disease burdens worldwide.Thus,further research is needed to lessen the risk of CVD death in HCC patient survivors.METHODS This study was conducted on the basis of the Surveillance,Epidemiology,and End Results database and included HCC patients with a diagnosis period from 2010 to 2015.The independent risk factors were identified using the Fine-Gray model.A nomograph was constructed to predict the CVM in HCC patients.The nomograph performance was measured using Harrell’s concordance index(C-index),calibration curve,receiver operating characteristic(ROC)curve,and area under the ROC curve(AUC)value.Moreover,the net benefit was estimated via decision curve analysis(DCA).RESULTS The study included 21545 HCC patients,of whom 619 died of CVD.Age(<60)[1.981(1.573-2.496),P<0.001],marital status(married)[unmarried:1.370(1.076-1.745),P=0.011],alpha fetoprotein(normal)[0.778(0.640-0.946),P=0.012],tumor size(≤2 cm)[(2,5]cm:1.420(1.060-1.903),P=0.019;>5 cm:2.090(1.543-2.830),P<0.001],surgery(no)[0.376(0.297-0.476),P<0.001],and chemotherapy(none/unknown)[0.578(0.472-0.709),P<0.001]were independent risk factors for CVD death in HCC patients.The discrimination and calibration of the nomograph were better.The C-index values for the training and validation sets were 0.736 and 0.665,respectively.The AUC values of the ROC curves at 2,4,and 6 years were 0.702,0.725,0.740 in the training set and 0.697,0.710,0.744 in the validation set,respectively.The calibration curves showed that the predicted probab-ilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities.DCA demonstrated that the prediction model has a high net benefit.CONCLUSION Risk factors for CVD death in HCC patients were investigated for the first time.The nomograph served as an important reference tool for relevant clinical management decisions. 展开更多
关键词 Hepatocellular carcinoma Cardiovascular disease deaths Fine-Gray model risk factor NOMOGRAPH PREDICT
下载PDF
Risk factors of non-arteritic anterior ischaemic optic neuropathy and central retinal artery occlusion
19
作者 Chu-Han Ma Cong-Yao Wang +2 位作者 Ting-Ting Dai Ting-Ting Chen Wen-Hui Zhu 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2024年第5期869-876,共8页
AIM:To investigate the difference in risk factors between non-arteritic anterior ischaemic optic neuropathy(NAION)and central retinal artery occlusion(CRAO)and develop a predictive diagnostic nomogram.METHODS:The stud... AIM:To investigate the difference in risk factors between non-arteritic anterior ischaemic optic neuropathy(NAION)and central retinal artery occlusion(CRAO)and develop a predictive diagnostic nomogram.METHODS:The study included 37 patients with monocular NAION,20 with monocular CRAO,and 24 with hypertension.Gender,age,and systemic diseases were recorded.Blood routine,lipids,hemorheology,carotid and brachial artery doppler ultrasound,and echocardiography were collected.The optic disc area,cup area,and cup-to-disc ratio(C/D)of the unaffected eye in the NAION and CRAO group and the right eye in the hypertension group were measured.RESULTS:The carotid artery intimal medial thickness(C-IMT)of the affected side of the CRAO group was thicker(P=0.039)and its flow-mediated dilation(FMD)was lower(P=0.049)than the NAION group.Compared with hypertension patients,NAION patients had higher whole blood reduced viscosity low-shear(WBRV-L)and erythrocyte aggregation index(EAI;P=0.045,0.037),and CRAO patients had higher index of rigidity of erythrocyte(IR)and erythrocyte deformation index(EDI;P=0.004,0.001).The optic cup and the C/D of the NAION group were smaller than the other two groups(P<0.0001).The diagnostic prediction model showed high diagnostic specificity(83.7%)and sensitivity(85.6%),which was highly related to hypertension,the C-IMT of the affected side,FMD,platelet(PLT),EAI,and C/D.CONCLUSION:CRAO patients show thicker C-IMT and worse endothelial function than NAION.NAION and CRAO may be related to abnormal hemorheology.A small cup and small C/D may be involved in NAION.The diagnostic nomogram can be used to preliminarily identify NAION and CRAO. 展开更多
关键词 non-arteritic anterior ischaemic optic neuropathy central retinal artery occlusion risk factors diagnostic prediction model NOMOGRAM
下载PDF
Understanding Osteoporosis: Pathophysiology, Risk Factors, Diagnosis, and Management
20
作者 Mahmoud Ismail 《Advances in Aging Research》 CAS 2024年第3期25-40,共16页
Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and deterioration of bone architecture, resulting in reduced bone strength and, consequently, increased susceptibility to fra... Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and deterioration of bone architecture, resulting in reduced bone strength and, consequently, increased susceptibility to fractures which poses a significant public health concern worldwide, particularly in aging populations [1]. The health-economic impact of vertebral and hip fractures has been extensively explored and it is well known that these fractures are associated with morbidity/disability and increased mortality;they also account for a substantial portion of the direct fracture costs. This review aims to provide a comprehensive overview of osteoporosis, including its pathophysiology, risk factors, diagnostic approaches, and management strategies. By elucidating the multifaceted nature of this condition, healthcare providers can better identify individuals at risk, implement preventive measures, and optimize treatment to reduce the burden of osteoporotic fractures. 展开更多
关键词 OSTEOPOROSIS Bone Mineral Density Fractures risk factors DIAGNOSIS MANAGEMENT FRAX (Fracture risk assessment Tool) Trabecular Bone Score (TBS)
下载PDF
上一页 1 2 250 下一页 到第
使用帮助 返回顶部