A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In con...A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.展开更多
Summary:Throughout the duration of the New Cooperative Medical Scheme(NCMS),it was found that an increasing number of rural patients were seeking out-of^county medical treatment,which posed a great burden on the NCMS ...Summary:Throughout the duration of the New Cooperative Medical Scheme(NCMS),it was found that an increasing number of rural patients were seeking out-of^county medical treatment,which posed a great burden on the NCMS fund.Our study was conducted to examine the prevalence of out-of^county hospitalizations and its related factors,and to provide a scientific basis for follow?up health insurance policies.A total of 215 counties in central and western China from 2008 to 2016 were selected.The total out-of-county hospitalization rate in nine years was 16.95%,which increased from 12.37%in 2008 to 19.21%in 2016 with an average annual growth rate of 5.66%.Its related expenses and compensations were shown to increase each year,with those in the central region being higher than those in the western region.Stepwise logistic regression reveals that the increase in out-of-county hospitalization rate was associated with region(XI),rural population(X2),per capita per year net income(X3),per capita gross domestic product(GDP)(X4),per capita funding amount of NCMS(X5),compensation ratio of out-of^county hospitalization cost(X6),per time average in-county(X7)and out-of-county hospitalization cost(X8).According to Bayesian network(BN),the marginal probability of high out-of^county hospitalization rate was as high as 81.7%.Out-of^county hospitalizations were directly related to X8,X3,X4 and X6.The probability of high out-of-county hospitalization obtained based on hospitalization expenses factors,economy factors,regional characteristics and NCMS policy factors was 95.7%,91.1%,93.0% and 88.8%,respectively.And how these factors affect out-of-county hospitalization and their interrelationships were found out.Our findings suggest that more attention should be paid to the influence mechanism of these factors on out-of-county hospitalizations,and the increase of hospitalizations outside the county should be reasonably supervised and controlled and our results will be used to help guide the formulation of proper intervention policies.展开更多
基金National Natural Science Foundation of China(No.61203184)
文摘A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.
文摘目前贝叶斯网络(Bayesian networks,BN)的传统结构学习算法在处理高维数据时呈现出计算负担过大、在合理时间内难以得到期望精度结果的问题.为了在高维数据下学习稀疏BN的最优结构,本文提出了一种学习稀疏BN最优结构的改进K均值分块学习算法.该算法采用分而治之的策略,首先采用互信息作为节点间距离度量,利用融合互信息的改进K均值算法对网络分块;其次,使用MMPC(Max-min parent and children)算法得到整个网络的架构,根据架构找到块间所有边的可能连接方向,从而找到所有可能的图结构;之后,对所有图结构依次进行结构学习;最终利用评分找到最优BN.实验证明,相比现有分块结构学习算法,本文提出的算法不仅习得了网络的精确结构,且学习速度有一定提高;相比非分块经典结构学习算法,本文提出的算法在保证精度基础上,学习速度大幅提高,解决了非分块经典结构学习算法无法在合理时间内处理高维数据的难题.
基金This work was supported by the National Natural Science Foundation of China(No.71573192 and No.81573262)the Fundamental Research Funds for the Central Universities,HUST(No.2016YXZD042).
文摘Summary:Throughout the duration of the New Cooperative Medical Scheme(NCMS),it was found that an increasing number of rural patients were seeking out-of^county medical treatment,which posed a great burden on the NCMS fund.Our study was conducted to examine the prevalence of out-of^county hospitalizations and its related factors,and to provide a scientific basis for follow?up health insurance policies.A total of 215 counties in central and western China from 2008 to 2016 were selected.The total out-of-county hospitalization rate in nine years was 16.95%,which increased from 12.37%in 2008 to 19.21%in 2016 with an average annual growth rate of 5.66%.Its related expenses and compensations were shown to increase each year,with those in the central region being higher than those in the western region.Stepwise logistic regression reveals that the increase in out-of-county hospitalization rate was associated with region(XI),rural population(X2),per capita per year net income(X3),per capita gross domestic product(GDP)(X4),per capita funding amount of NCMS(X5),compensation ratio of out-of^county hospitalization cost(X6),per time average in-county(X7)and out-of-county hospitalization cost(X8).According to Bayesian network(BN),the marginal probability of high out-of^county hospitalization rate was as high as 81.7%.Out-of^county hospitalizations were directly related to X8,X3,X4 and X6.The probability of high out-of-county hospitalization obtained based on hospitalization expenses factors,economy factors,regional characteristics and NCMS policy factors was 95.7%,91.1%,93.0% and 88.8%,respectively.And how these factors affect out-of-county hospitalization and their interrelationships were found out.Our findings suggest that more attention should be paid to the influence mechanism of these factors on out-of-county hospitalizations,and the increase of hospitalizations outside the county should be reasonably supervised and controlled and our results will be used to help guide the formulation of proper intervention policies.
