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Application of Partial Least Square Regression Model in the Study of Personality Characteristics of the Scientific Personnel

Yuanyuan WANG ; Jingwu CHEN

Chinese Journal of Health Statistics.2010;(1):21-24. doi:10.3969/j.issn.1002-3674.2010.01.006

Objective To explore the application of partial least square regression model in the study of personality characteristics of the scientific personne1,to supply more reliable and scientific rationale to the training and intelligent use of scientific personne1 in colleges.Methods We took our survey by 114 scientific personnel from eight universities with NEO-PI-R(Revised NEO Personality Inventory).After the process of simplifying the NEO-PI-R,we need partial least square regression model to analyze.Results The results of the PLS analysis indicates the goodness-of-fit of the model was good,which opened out clearly and intuitively the connection between the items and five personality characteristics,the model could be used as theory reference for personnel department,research department and education department to consummate the training of scientific personne1 in colleges.Conclusion Partial least square regression model could be used to study personality characteristics of the scientific personne1 in colleges as statistic method appearing recently.

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An Empirical Research on Hospital Informatization Level in China

Fangdong DU ; Zhenqiu SUN ; Keqin RAO

Chinese Journal of Health Statistics.2010;(1):35-39. doi:10.3969/j.issn.1002-3674.2010.01.010

Objective Constructing a hospital informatization level evaluation model and carry out an empirical research.Methods Based on experts' suggestions and comprehensive scoring method,a evaluation model of hospital informatization level has been formed,and 1,221 Chinese hospitals have been selected randomly in this empirical research.Results The evaluation model and rating scale is reliable,valid and sensitive,and it can be used to evaluate hospital informatization level at larger scope.The difference of informtization level between hospitals in different areas,different type is evident and the development situation in hospitals is imblalace also.Conclusion The hospilal should emphasis on the informatization,and improve the application of information technology.

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Effect of Coping Style and Social Adjustment on Mental Health of Undergraduates

Qiaoyu JIANG ; Nengfeng XU ; Jianping CAO

Chinese Journal of Health Statistics.2010;(1):25-27. doi:10.3969/j.issn.1002-3674.2010.01.007

Objective To explore the effect of coping style and social adjustment on mental health of undergraduates.Methods A large sample investigation was operated on undergraduates in Fuzhou.Linear correlation and regression analysis and structural equation analysis were used in explosing of the effect of coping style and social adjustment on mental health of undergraduates.Results A 38.9% variance of meatal health could be explained by active and negative coping style,objective and subjective social adjustment.Active and negative coping style demonstrated respectively 59.3%,46.7% indirect effect of total effect through social adjustment on mental health.Conclusion To advocate active coping style and improve college social adjusment were good for the promotion of mental health.

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The Impact of Sample-size and Sample-process on Several Usual Importance Evaluate Methods

Lizhi WU ; Xiaoxia JIA ; Qijun SHEN

Chinese Journal of Health Statistics.2017;34(2):210-213.

Objective Implement random sample from a simulation population,to evaluate the The impact of samplesize and sample-process on several usual importance evaluate methods,observe the stability of those methods.Methods This study introduced existed importance methods,using PROC SURVEYSELECT procedure to sample a fixed population for 1000 times,generating 1000 same size sample,to evaluate the stability of relative importance methods.We sampled the population to generate datasets with different sample size to observe impact of sample-size on those methods.Results The sum of squared correlation coefficients' estimator is bigger than model R-square,squared standardized regression coefficients' sum is smaller.In contrary,sum of the Product Measure,Relative Weight and Dominance Analysis are extremely close to model R-square.When the sample size small than 1000,the estimator have obviously variation,but the variation decreased when the sample size rise up.Conclusion The dominance analysis has best stability,also has the best match of model R2 in those methods.

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The Economic Burden of Patients with Mental Illness in Shandong Province during 2005 to 2013

Junfang XU ; Jian WANG ; Feng CHENG

Chinese Journal of Health Statistics.2017;34(2):196-199.

Objective To calculate the social economic burden of mental illness,and to analyze the economic impact of different patients with mental illness in Shandong Province.Methods Direct method was used to calculate the direct economic burden,and human capital method was employed to estimate the indirect economic burden.Results The social economic burden increased from 10.076 billion in 2005 to 31.277 billion in 2013,and the proportion of the social economic burden accounting for GDP of Shandong province was between 0.5 ％～ 0.7％.The economic burden caused by women,18-39 years old patients,rural patients and mood disorders was higher than that of men,more than 55 years-old patients,urban patients and other diagnoses,respectively.Conclusions The economic burden brought by mental illness was heavy and increasing during 2005-2013.The economic burden caused by different people was heterogeneous.

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A Comparative Study on the Effect of Principal Component Regression Analysis and Projection Pursuit Regression Analysis Applied to the Data with Collinearity

Wan HU ; Yansong SUN ; Liangping HU

Chinese Journal of Health Statistics.2017;34(2):192-195.

Objective To compare the difference of effect between principal component regression analysis and projection pursuit regression analysis when collinearity exists in data.Methods Evaluating the advantages and disadvantages of the two modeling methods by using the actual data on two aspects:the fitting effect and the predicting effect.Results The principal component regression model showed that the coefficient of determination was 0.8172,the mean of absolute relative error was 6.42％ and the mean square of prediction error was 0.61.The projection pursuit regression model,on the other hand,showed that the coefficient of determination ranged from 0.8851 to 0.9944,the mean of absolute relative error ranged from1.11％ to 4.81％ and the mean square of prediction error ranged from 0.03 to 0.38.Conclusion The analysis results based on the actual data with collinearity indicate that the projection pursuit regression analysis outperforms the principal component regression analysis both in fitting and predicting effect.

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Temporal Trend and Prediction of Mortality of Life Lost due to Esophageal Cancer in Residents in Tieling

Liang ZHANG ; Wen LU ; Xia ZHAO

Chinese Journal of Health Statistics.2017;34(2):247-249.

Objective To explore the trend of mortality and years of life lost due to Esophageal Cancer in residents in Tieling,so as to provide the basis data on preventing Esophageal cancer in Tieling.Methods The data of residents in Tieling dying of Esophageal cancer from 2007 to 2015 was collected and cleared up to calculate the evaluation indexes including the mortality rate,the average percentage change of mortality rate.GM(1,1) model was used to predict the future mortality.Results From 2007 to 2015,the Average Esophageal cancer Mortality Rate of in residents in Tieling was 5.26 per 100000 persons,and especially 1.95％ raised a year.The Mortality Rate would increase from 2016 to 2019.Conclusion Tieling Esophageal Cancer mortality rate is on the rise,especially for elder men more than 60.So that the proper prevention measures should be car ried and strengthened.

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Building a Prediction System of Influenza Epidemics with LASSO Regression Model and Baidu Search Query Data

Pi GUO ; Li WANG ; Yuantao HAO

Chinese Journal of Health Statistics.2017;34(2):186-191.

Objective To evaluate the performance of a prediction system built with LASSO regression model and Baidu search query data.Methods Based on a strategy using a combination of Bagging and multi-measure optimization method,this study proposed an ensemble LASSO regression model which had an obviously improved performance,and applied it to predict the epidemics of influenza in China.Results The results showed that the improved model had significantly smaller prediction error rates than that of the conventional LASSO regression model for influenza cases during the study period of 2011-2015.This study designed an open source R package,SparseLearner,which was conveniently used and further developed.Conclusion The combination of Bagging and multi-measure optimization method is an efficient strategy to improve the performance of LASSO regression model.The proposed ensemble LASSO regression model in this study can be applied for the prediction of infectious diseases epidemics.

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ARIMA Product Season Model for Predicting Number of Inpatient and Hospitalized Expense of Malignant Tumor

Ling CHEN ; Lijun CHENG ; Xiangjun ZHAO

Chinese Journal of Health Statistics.2017;34(4):554-557.

Objective To explore the application of auto-regressive integrated moving average (ARIMA) product season model in predicting number of inpatient and hospitalized expense of malignant tumor,and to provide scientific basis for hospital business management.Methods We collected inpatient data of malignant tumor from January 2007 to December 2015 in one hospital for model fitting,and used monthly data 2016 to verify the effect of model prediction.We predicted the number of inpatient and hospitalized expense of malignant tumor in 2017.Results ARIMA (0,1,1) (1,1,0) 12 was the best model for number of inpatient and hospitalized expense of malignant tumor,with prediction fitting errors of 1.1％ and 1.47 ％,respectively.The number of inpatient and hospitalized expense of malignant tumor in 2017 were predicted to be 7631 and 0.336 billion.Conclusion ARIMA product season model can better applied in the predicting of hospital business management.

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Analysis of Quality of Life and its Influencing Factors in Patients with Acute Upper Respiratory Tract Infection

Aixia MA ; Qiang LIU ; Hongchao LI

Chinese Journal of Health Statistics.2017;34(4):550-553.

Objective To learn quality of life of acute upper respiratory tract infection patients and their influence factors.Methods We conducted questionnaire investigation on acute upper respiratory tract infection patients from five hospitals,respectively located in Wuhan,Hefei and Jinhua.The scale we used is EQ-5D.We calculated EQ-5D score through three different integration systems,which are from China,Korea and the UK,in order to compare difference among different countries' integration systems.Then econometric model was used to carry out regression analysis on factors affected EQ-5D score.Results 659 samples was included,with 319 from Wuhan,235 from Hefei and 105 from Jinhua.Among EQ-5D five dimensions,the first three dimensions do not have problems.However,81.94％ of the patients have problems in pain/discomfort and 47.8％ of them have problems in anxiety/depression.In regression analysis,location,severity level of disease and whether accompanied by chronic diseases or not have influence on EQ-5D score.Compared with utilities of patients who are suffered from other diseases,utilities of acute upper respiratory tract infection patients are lower than that of cerebral apoplexy,hypertension,coronary heart diseaseand diabetes patients;but are higher than that of Chronic lymphatic filariasis,chronic obstructive pneumonia and rheumatoid arthritis patients.Conclusion Chinese acute upper respiratory tract infection patients mainly have problems in the dimension of pain/discomfort and anxiety/depression.Compared to British integration system,Korean's is more applicable to Chinese population.Acute upper respiratory tract infection patients' utilities are lower than that of healthy population and chronic invalids with no obvious symptoms,and higher than that of chronic invalids with obvious symptoms.

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