Forecasting Loss of Ecosystem Service Value Using a BP Network： A Case Study of the Impact of the South-to-north Water Transfer Project on the Ecological Environmental in Xiangfan, Hubei Province, China

Objective To recognize and assess the impact of the South-to-north Water Transfer Project(SNWTP) on the ecological environment of Xiangfan, Hubei Province, situated in the water-out area,and develop sound scientific countermeasures. Methods A three-layer BP network was built to     

Characteristics and Risk factors of Psychosomatic Symp－toms Related to Female Tubal Sterilization in Rural Area in Hunan Province,China：A Prospective Study

Objectives:To study characteristics of psychosomatic symptoms related to sterilization,to find out risk factors and their roles ascribed to psychosomatic symptoms,and to establish a mathematic model for screening out susceptible women.Methods:This study enrolled 776 women in rural area at three counties of Linxiang,Qiyang,Changsha of Hunan province in China between February 1990 and April 1992.Brief Neurosis Screening Scale(BNSS),Symptom Checklist 90(SCL-90),sensitivity to pain,suggestibility were used to indicate subjects‘psychological status.Logistic regression model and retrograde discriminant analysis were applied to develop a mathematical model.Results:Prevalence of psychosomatic reactions or symptoms was 54.8% before steril-ization,26.6% at three months and 16.4% at one year after operation respectively.Psychosomatic symptoms were verified to be the result of joint effects of multiple risk factors.The following risk factors were associated with postoperative symptoms:anger-hostility(RR=33.71),high suggestibility(RR=4.53),high neuroticism(RR=3.44),sensitivity to pain(RR=2.14) and operative sites(RR=2.05).A mathe-matical model to estimate the probability of developing psychosomatic symptoms in ster-ilization was established.Conclusions:More than half of women suffered from psychosomatic reactions before operation,and some of them did not recover after operation.The postoperative psycho-somatic symptoms are the joint effect of multiple risk factors.     

Physical Growth of Children in Urban,Suburban and Rural Mainland China：A Study of 20 Years＇ Change

Objective To describe secular trends on physical growth of children in China during the year of 1985‐2005 and to analyze the urban‐suburban‐rural difference and its change. Methods The measurements of height, weight and chest circumference obtained from two serial national cross‐sectional surveys for children aged 0 to 7 years in China were used to analyze the secular trends, and the growth differences among urban, suburban and rural children were compared. Results The average weight and height for both boy...     

Using Support Vector Machine to Predict Eco-environment Burden:A Case Study of Wuhan,Hubei Province,China1

Objective The human socio-economic development depends on the planet's natural capital.Humans have had a considerable impact on the earth,such as resources depression and environment deterioration.The objective of this study was to assess the impact of socio-eeonomic development on the ecological environment of Wuhan,Hubei Province,China,during the general planning period 2006-2020. Methods Support vector machine(SVM) model was constructed to simulate the process of eco-economic system of Wuhan.Socio-economic factors of urban total ecological footprint(TEF)were selected by partial least squares(PLS)and leave-one-out cross validation(LOOCV).Historical data of socio-economic factors as inputs,and corresponding historical data of TEF as target outputs.were presented to identify and validate the SVM model.When simulated as output in succession. Results Up to 2020,the district would have suffered an accumulative TEF of 28.374 million gha,which was over 1.5 times that of 2004 and healrly 3 times that of 1988.The per capita EF would be up to 3.019 gha in 2020. Conclusions The simulation indicated that although the increase rate of GDP would be restricted in a lower level during the general planning period,urban ecological environment burden could not respond to the socio-economic circumstances promptly.SVM provides tools for dynamic assessment of regional eco-environment.However,there still exist limitations and disadvantages in the model.We believe that the next logical step in deriving better dynamic models of ecosystem is to integrate SVM and other algorithms or technologies.     

基于文化差分进化算法的最小二乘支持向量机及QSAR建模

针对最小二乘支持向量机最佳算法参数难以确定的缺陷,提出了基于文化差分进化算法的最小二乘支持向量机(Cultural Differential evolution Algorithm Least Square Support Vector Machine,CDE-LSSVM)。该算法通过新型的文化差分进化算法优化确定最小二乘支持向量机核宽度参数和惩罚系数,建立具有良好预测性能的模型。同时,针对药物定量构效关系（Quantitative Structure-Activity Relationships,QSAR）模型具有高度非线性、变量之间存在相关性的特征,采用CDE-LSSVM建立HIV-1蛋白酶抑制剂的药物定量构效关系模型。模型具有很好的拟合精度与预测精度,且优于最小二乘支持向量机、BP神经网络和径向基神经网络。     

Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions

Background

Electronic health record (EHR) users must regularly review large amounts of data in order to make informed clinical decisions, and such review is time-consuming and often overwhelming. Technologies like automated summarization tools, EHR search engines and natural language processing have been shown to help clinicians manage this information.

Objective

To develop a support vector machine (SVM)-based system for identifying EHR progress notes pertaining to diabetes, and to validate it at two institutions.

Materials and methods

We retrieved 2000 EHR progress notes from patients with diabetes at the Brigham and Women''s Hospital (1000 for training and 1000 for testing) and another 1000 notes from the University of Texas Physicians (for validation). We manually annotated all notes and trained a SVM using a bag of words approach. We then used the SVM on the testing and validation sets and evaluated its performance with the area under the curve (AUC) and F statistics.

Results

The model accurately identified diabetes-related notes in both the Brigham and Women''s Hospital testing set (AUC=0.956, F=0.934) and the external University of Texas Faculty Physicians validation set (AUC=0.947, F=0.935).

Discussion

Overall, the model we developed was quite accurate. Furthermore, it generalized, without loss of accuracy, to another institution with a different EHR and a distinct patient and provider population.

Conclusions

It is possible to use a SVM-based classifier to identify EHR progress notes pertaining to diabetes, and the model generalizes well.