Background There have been numerous intervention studies focusing on the development of preterm infants,but there has been limited investigation into the home environment as a determinant of developmental outcomes in ...Background There have been numerous intervention studies focusing on the development of preterm infants,but there has been limited investigation into the home environment as a determinant of developmental outcomes in preterm infants.The aspects and extent to which the home environment affects the early(18 months corrected age)neuropsychological development of preterm infants are still unclear.Aims This study aimed to analyse the effect of the home environment on the neuropsychiatric development of preterm infants at 18 months corrected age after discharge from the neonatal intensive care unit(NICU).It also sought to provide a basis for promoting neuropsychiatric development among preterm infants by improving the home environment.Methods In this retrospective cross-sectional study,275 preterm infants born between January 2019 and January 2022 were followed up for systematic management after discharge from the NICU at Shanghai Children's Hospital.The Home Nurture Environment Questionnaire was used to assess the home environment of the infants and analyse its impact on the developmental quotient(evaluated by the Gesell Developmental Scale)and the rate of developmental delays at 18 months corrected age.Results A total of 41.454%of the infants were extremely preterm.The developmental quotient scores at 18 months corrected age were in the middle of the scale.The language domain had the highest rate of developmental delay(46.182%),followed by the adaptive domain(37.091%).Multiple logistic regression analyses showed that compared with infants in supportive home environments,infants with moderate/unsupportive home environments had significantly elevated risks of development delay:2.162-fold for global(odds ratio(OR)2.162,95% confidence interval(Cl)1.274 to 3.665,p=0.004),2.193-fold for fine motor(OR 2.193,95%CI 1.161 to 4.140,p=0.016),2.249-fold for language(0R 2.249,95%CI 1.336 to 3.786,p=0.002)and 2.042-fold for personal-social(OR 2.042,95%CI 1.149 to 3.628,p=0.015).Conclusions A supportive home environment is a crucial protective factor for the neuropsychological development of preterm infants.It is associated with higher developmental quotient scores and protects against neuropsychiatric delays.Incorporating evaluation and continuous improvement of the home environment into the management framework for preterm infants to promote optimal neurodevelopment is essential.展开更多
Background:Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years.This study aimed to validate the use of the artificial neural network(ANN)model to predict the 5?year mortalit...Background:Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years.This study aimed to validate the use of the artificial neural network(ANN)model to predict the 5?year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model,multiple logistic regression(MLR)model,and Cox regression model.Methods:This study compared the MLR,Cox,and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and 2010.An estimation dataset was used to train the model,and a validation dataset was used to evaluate model performance.The sensitivity analysis was also used to assess the relative signifi?cance of input variables in the prediction model.Results:The ANN model significantly outperformed the MLR and Cox models in predicting 5?year mortality,with higher overall performance indices.The results indicated that the 5?year postoperative mortality of breast cancer patients was significantly associated with age,Charlson comorbidity index(CCI),chemotherapy,radiotherapy,hormone therapy,and breast cancer surgery volumes of hospital and surgeon(all P<0.05).Breast cancer surgery volume of surgeon was the most influential(sensitive)variable affecting 5?year mortality,followed by breast cancer surgery volume of hospital,age,and CCI.Conclusions:Compared with the conventional MLR and Cox models,the ANN model was more accurate in predict?ing 5?year mortality of breast cancer patients who underwent surgery.The mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.展开更多
As the population ages, Alzheimer’s disease is rapidly increasing, and the diagnosis of the disease is still poorly understood. In comparison to cancer, 90% of patients become aware of their diagnosis, but only 45% o...As the population ages, Alzheimer’s disease is rapidly increasing, and the diagnosis of the disease is still poorly understood. In comparison to cancer, 90% of patients become aware of their diagnosis, but only 45% of the people with Alzheimer’s are aware. Thus, the need for biomarkers for reliable diagnosis is tremendous to help in finding treatment for this serious disease. Hence, the main aim of this paper is to utilize information from baseline measurements to develop a statistical prediction model using multiple logistic regression to distinguish Alzheimer’s disease patients from cognitively normal individuals. Our optimal predictive model includes six risk factors and two interaction terms and has been evaluated using classification accuracy, sensitivity, specificity values and area under the curve.展开更多
The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents(RTAs)on rural roads.Multiple Logistic Regression(MLR)was used to predict the likelih...The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents(RTAs)on rural roads.Multiple Logistic Regression(MLR)was used to predict the likelihood of RTAs.For more accurate prediction,Multi-Layer Perceptron(MLP)and Radius Basis Function(RBF)neural networks were applied.Results indicated that in MLR,the model obtained from the backward method with the correct percent of 84.7%and R2 value of 0.893 was the best method for predicting the likelihood of RTAs.Also,MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead,followed byand then vehicle-motorcycle/bike accidents were the greatest problems.Among the models,MLP had a better performance,so that the prediction accuracy of MLR,MLP,and RBF were 84.7%,96.7%,and 92.1%,respectively.MLP model,due to higher accuracy,showed that the variable of reason of accident had the highest effect on the prediction of accidents,and considering MLR results,the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents.Therefore,motorcyclists and cyclists are more prone to accidents,and appropriate solutions should be adopted to enhance their safety.展开更多
The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics h...The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.展开更多
基金funded by Shanghai Municipal Health and Wellness Commission Health Industry Clinical Research Special Project(202140299).
文摘Background There have been numerous intervention studies focusing on the development of preterm infants,but there has been limited investigation into the home environment as a determinant of developmental outcomes in preterm infants.The aspects and extent to which the home environment affects the early(18 months corrected age)neuropsychological development of preterm infants are still unclear.Aims This study aimed to analyse the effect of the home environment on the neuropsychiatric development of preterm infants at 18 months corrected age after discharge from the neonatal intensive care unit(NICU).It also sought to provide a basis for promoting neuropsychiatric development among preterm infants by improving the home environment.Methods In this retrospective cross-sectional study,275 preterm infants born between January 2019 and January 2022 were followed up for systematic management after discharge from the NICU at Shanghai Children's Hospital.The Home Nurture Environment Questionnaire was used to assess the home environment of the infants and analyse its impact on the developmental quotient(evaluated by the Gesell Developmental Scale)and the rate of developmental delays at 18 months corrected age.Results A total of 41.454%of the infants were extremely preterm.The developmental quotient scores at 18 months corrected age were in the middle of the scale.The language domain had the highest rate of developmental delay(46.182%),followed by the adaptive domain(37.091%).Multiple logistic regression analyses showed that compared with infants in supportive home environments,infants with moderate/unsupportive home environments had significantly elevated risks of development delay:2.162-fold for global(odds ratio(OR)2.162,95% confidence interval(Cl)1.274 to 3.665,p=0.004),2.193-fold for fine motor(OR 2.193,95%CI 1.161 to 4.140,p=0.016),2.249-fold for language(0R 2.249,95%CI 1.336 to 3.786,p=0.002)and 2.042-fold for personal-social(OR 2.042,95%CI 1.149 to 3.628,p=0.015).Conclusions A supportive home environment is a crucial protective factor for the neuropsychological development of preterm infants.It is associated with higher developmental quotient scores and protects against neuropsychiatric delays.Incorporating evaluation and continuous improvement of the home environment into the management framework for preterm infants to promote optimal neurodevelopment is essential.
基金supported by funding from“the Ministry of Science and Technology”in Taiwan,China(MOST 102-2314-B-037-043)
文摘Background:Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years.This study aimed to validate the use of the artificial neural network(ANN)model to predict the 5?year mortality of breast cancer patients after surgery and compare predictive accuracy between the ANN model,multiple logistic regression(MLR)model,and Cox regression model.Methods:This study compared the MLR,Cox,and ANN models based on clinical data of 3632 breast cancer patients who underwent surgery between 1996 and 2010.An estimation dataset was used to train the model,and a validation dataset was used to evaluate model performance.The sensitivity analysis was also used to assess the relative signifi?cance of input variables in the prediction model.Results:The ANN model significantly outperformed the MLR and Cox models in predicting 5?year mortality,with higher overall performance indices.The results indicated that the 5?year postoperative mortality of breast cancer patients was significantly associated with age,Charlson comorbidity index(CCI),chemotherapy,radiotherapy,hormone therapy,and breast cancer surgery volumes of hospital and surgeon(all P<0.05).Breast cancer surgery volume of surgeon was the most influential(sensitive)variable affecting 5?year mortality,followed by breast cancer surgery volume of hospital,age,and CCI.Conclusions:Compared with the conventional MLR and Cox models,the ANN model was more accurate in predict?ing 5?year mortality of breast cancer patients who underwent surgery.The mortality predictors identified in this study can also be used to educate candidates for breast cancer surgery with respect to the course of recovery and health outcomes.
文摘As the population ages, Alzheimer’s disease is rapidly increasing, and the diagnosis of the disease is still poorly understood. In comparison to cancer, 90% of patients become aware of their diagnosis, but only 45% of the people with Alzheimer’s are aware. Thus, the need for biomarkers for reliable diagnosis is tremendous to help in finding treatment for this serious disease. Hence, the main aim of this paper is to utilize information from baseline measurements to develop a statistical prediction model using multiple logistic regression to distinguish Alzheimer’s disease patients from cognitively normal individuals. Our optimal predictive model includes six risk factors and two interaction terms and has been evaluated using classification accuracy, sensitivity, specificity values and area under the curve.
文摘The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents(RTAs)on rural roads.Multiple Logistic Regression(MLR)was used to predict the likelihood of RTAs.For more accurate prediction,Multi-Layer Perceptron(MLP)and Radius Basis Function(RBF)neural networks were applied.Results indicated that in MLR,the model obtained from the backward method with the correct percent of 84.7%and R2 value of 0.893 was the best method for predicting the likelihood of RTAs.Also,MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead,followed byand then vehicle-motorcycle/bike accidents were the greatest problems.Among the models,MLP had a better performance,so that the prediction accuracy of MLR,MLP,and RBF were 84.7%,96.7%,and 92.1%,respectively.MLP model,due to higher accuracy,showed that the variable of reason of accident had the highest effect on the prediction of accidents,and considering MLR results,the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents.Therefore,motorcyclists and cyclists are more prone to accidents,and appropriate solutions should be adopted to enhance their safety.
基金supported by the National Natural Science Foundation of China Key Project under Grant No.70933003the National Natural Science Foundation of China under Grant Nos.70871109 and 71203247
文摘The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.
