Arterial steroid injection therapy can inhibit the progression of severe acute hepatic failure toward fulminant liver failure

AIM: To utilize transcatheter arterial steroid injection therapy (TASIT) via the hepatic artery to reduce hepatic macrophage activity in patients with severe acute hepatic failure.METHODS: Thirty-four patients with severe acute hepatic failure were admitted to our hospital between June 2002 to June 2006 providing for the possibility of liver transplantation (LT). Seventeen patients were treated using traditional liver supportive procedures, and the other 17 patients additionally underwent TASIT with 1000 mg methylprednisolone per day for 3 continuous days. RESULTS: Of the 17 patients who received TASIT, 13 were cured without any complications, 2 died, and 2 underwent LT. Of the 17 patients who did not receive TASIT, 4 were self-limiting, 7 died, and 6 underwent LT. Univariate logistic analysis revealed that ascites, serum albumin, prothrombin time, platelet count, and TASIT were significant variables for predicating the prognosis. Multivariate logistic regression analysis using stepwise variable selection showed that prothrombin time, platelet count, and TASIT were independent predictive factors. CONCLUSION: TASIT might effectively prevent the progression of severe acute hepatic failure to a fatal stage of fulminant liver failure.

Predictive model for 5.year mortality after breast cancer surgery in Taiwan residents

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.

Alzheimer’s Disease: The Relative Importance Diagnostic

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.

Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

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.

PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

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.