An initial study of information seeking behavior of researchers as faculty/student team members

Purpose:This study was carried out to uncover the characteristics of information seeking behavior of researchers as faculty/student team members.Design/methodology/approach:An inventory encompassing 6 dimensions of information seeking behavior was developed:Information awareness,information acquisition,information evaluation,information organization and management,information utilization and information ethics.Data was collected on 306 respondents from 52 faculty/student teams in Central South University in China and analyzed using SPSS 18.0 software.Findings:Significant differences were found among researchers with different genders in information awareness and in different academic disciplines in information acquisition and information utilization.The survey shows the characteristics of information seeking behavior of different gender groups and different teams:1) male participants got higher scores in all of the 6 dimensions of information seeking behavior;2) small teams performed best,followed by middle-sized teams and large teams;3) faculty/doctoral student teams possessed better information seeking skills than faculty/master’s student teams or faculty/doctoral and master’s student teams:4) medical teams achieved the highest level in all of the 6 dimensions of information seeking behavior,whereas natural science teams the lowest level.Medical and engineering teams were rated higher than other teams in information acquisition and information utilization.Research limitations:The small population size and doctoral students accounting for only a small portion of the respondents in the sample limit the generalization of our findings.Practical implications:The findings of this study have some implications for research and practice,especially for educational institutions,library science and information literacy training.Originality/value:This paper is the first to describe and analyze the characteristics of information seeking behavior of researchers as faculty/student team members.

Research on classification diagnosis model of psoriasis based on deep residual

Objective A classification and diagnosis model for psoriasis based on deep residual network is proposed in this paper.Which using deep learning technology to classify and diagnose psoriasis can help reduce the burden of doctors,simplify the diagnosis and treatment process,and improve the quality of diagnosis.Methods Firstly,data enhancement,image resizings,and TFRecord coding are used to preprocess the input of the model,and then a 34-layer deep residual network(ResNet-34)is constructed to extract the characteristics of psoriasis.Finally,we used the Adam algorithm as the optimizer to train ResNet-34,used cross-entropy as the loss function of ResNet-34 in this study to measure the accuracy of the model,and obtained an optimized ResNet-34 model for psoriasis diagnosis.Results The experimental results based on k-fold cross validation show that the proposed model is superior to other diagnostic methods in terms of recall rate,F1-score and ROC curve.Conclusion The ResNet-34 model can achieve accurate diagnosis of psoriasis,and provide technical support for data analysis and intelligent diagnosis and treatment of psoriasis.

Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm

AIM： To figure out the contributed factors of the hospitalization expenses of senile cataract patients（HECP） and build up an area-specified senile cataract diagnosis related group（DRG） of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund.METHODS： The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector（E-CHAID） model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc.RESULTS： The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases.CONCLUSION： The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.

Older age at first birth is a risk factor for pancreatic cancer： a meta-analysis

BACKGROUND： Some studies found that age at first birth is associated with pancreatic cancer; others did not. The present meta-analysis was to evaluate the relationship between age at first birth and pancreatic cancer in women.DATA SOURCES： We searched Pub Med, Embase, and the Cochrane Library for relevant publications on age at first birth and pancreatic cancer up to April, 2014. The eligible studies（six cohorts and five case-controls） were independently selected by two authors. Pooled relative risk（RR） estimates and corresponding 95% confidence interval（95% CI） were calculated using the inverse-variance method.RESULTS： The pooled RR of pancreatic cancer risk for the highest versus lowest categories of age at first birth was 1.21（95% CI： 1.01-1.45, P=0.314, I^2=13.7%）. Consistent relationships were also observed within subgroup analyses stratified by study design, geographic region, and whether the studies included adjustment for cigarette smoking, diabetes, or all of the confounders. In this meta-analysis, no publication bias among studies was observed using Egger＇s test（P=0.383） or Begg＇s test（P=0.436）.CONCLUSION： Our findings suggest that older age at first birth is associated with an increased risk of pancreatic cancer in women and the exact functional mechanism needs further investigation.