Objective To predict the incidence of anxiety in Alzheimer’s disease (AD) patients by using machine-learning models. Methods A large randomized controlled clinical trial was analyzed in this study, which involved AD ...Objective To predict the incidence of anxiety in Alzheimer’s disease (AD) patients by using machine-learning models. Methods A large randomized controlled clinical trial was analyzed in this study, which involved AD patients and caregivers from 6 different sites in the United States. The incidence of anxiety in AD patients was predicted by backpropagation artificial neural networks and several machine learning models, including Bayesian Networks, logistic regression, ADTree, J48, and Decision table. Results Among all models for predicting the incidence of anxiety in AD patients, the artificial neural network with respectively 6 and 3 neurons in the first and second hidden layers achieved the highest predictive accuracy of 85.56 %. The decision tree revealed three main risk factors: "caregiver experiencing psychological distress", "caregiver suffering from chronic disease or cancer", and "lack of professional care service". Conclusion The unique ability of artificial neural networks on classifying nonlinearly separable problems may substantially benefit the prediction, prevention and early intervention of anxiety in Alzheimer’s patients. Decision tree has the double efficacy of predicting the incidence and discovering the risk factors of anxiety in Alzheimer’s patients. More resources should be provided to caregivers to improve their mental and physical health, and more professional care services should be adopted by Alzheimer’s families.展开更多
This paper investigates a global asymptotic regulation control problem for a class of nonlinear systems with dynamic uncertainties.The requirement of a priori knowledge of control directions is removed and the inverse...This paper investigates a global asymptotic regulation control problem for a class of nonlinear systems with dynamic uncertainties.The requirement of a priori knowledge of control directions is removed and the inverse dynamics satisfy the weaker integral input-to-state stable condition.By application of the changing supply rates and the Nussbaum-type gain techniques,a partial state-feedback regulator is constructed.The main results demonstrate that the designed controller ensures the system state converges to the origin whereas the other signals of the closed-loop system are bounded. Simulation results are illustrated to show the effectiveness of the proposed approach.展开更多
基金the 2006 Mountaineering Program of Shanghai, China(06JC14043).
文摘Objective To predict the incidence of anxiety in Alzheimer’s disease (AD) patients by using machine-learning models. Methods A large randomized controlled clinical trial was analyzed in this study, which involved AD patients and caregivers from 6 different sites in the United States. The incidence of anxiety in AD patients was predicted by backpropagation artificial neural networks and several machine learning models, including Bayesian Networks, logistic regression, ADTree, J48, and Decision table. Results Among all models for predicting the incidence of anxiety in AD patients, the artificial neural network with respectively 6 and 3 neurons in the first and second hidden layers achieved the highest predictive accuracy of 85.56 %. The decision tree revealed three main risk factors: "caregiver experiencing psychological distress", "caregiver suffering from chronic disease or cancer", and "lack of professional care service". Conclusion The unique ability of artificial neural networks on classifying nonlinearly separable problems may substantially benefit the prediction, prevention and early intervention of anxiety in Alzheimer’s patients. Decision tree has the double efficacy of predicting the incidence and discovering the risk factors of anxiety in Alzheimer’s patients. More resources should be provided to caregivers to improve their mental and physical health, and more professional care services should be adopted by Alzheimer’s families.
基金supported by the National Natural Science Foundation of China under Grant Nos.60674027, 60974127,and 60904022the Key Project Foundation of the Educational Ministry under Grant No.208074the Innovation Program of Graduate Students of Jiangsu Province of China under Grant No.CXZZ11_0155
文摘This paper investigates a global asymptotic regulation control problem for a class of nonlinear systems with dynamic uncertainties.The requirement of a priori knowledge of control directions is removed and the inverse dynamics satisfy the weaker integral input-to-state stable condition.By application of the changing supply rates and the Nussbaum-type gain techniques,a partial state-feedback regulator is constructed.The main results demonstrate that the designed controller ensures the system state converges to the origin whereas the other signals of the closed-loop system are bounded. Simulation results are illustrated to show the effectiveness of the proposed approach.
