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Articles by K. Chen
Total Records ( 2 ) for K. Chen
  X. Sun , K. Chen , E.P. Berg and J.D. Magolski
  This study investigates the usefulness of electronically derived and analyzed fresh beef lean color image features for predicting official Chinese beef color scores. About 160 beef longissimus thoracis (ribeye) cross-section images were collected. The twelve features of beef muscle color were extracted and one feature was calculated using stepwise multiple regression analysis. Multiple linear regression and SVM model with inputs of color features and outputs of 4-7 color scores, respectively were designed to automatically estimate the grade of beef muscle color. Multiple linear regression analysis of the coefficient of determination (R2 = 0.89) and the model accuracy which determine the beef color muscle scores is 86.8%. SVM classifier achieved the best performance percentage of 94.7% showing that the machine vision combined with SVM discrimination method can provide an effective tool for predicting color scores of beef muscle.
  W Junfang , Z Biao , Z Weijun , S Zhang , W Yinyin and K. Chen
  Background

In this study, we determined the prevalence of unmet need for hospitalization service and the characteristics of the elderly with this unmet need in Zhejiang province, China.

Methods

Data were collected from a random sample of 4046 Chinese aged 60 years and older in Zhejiang province. Based on the Andersen-Newman service utilization framework, multivariable logistic regression analysis was used to determine independent effects of these variables on the likelihood of having an unmet need for hospitalization service.

Results

Overall, the prevalence of unmet need was 16.2% for hospitalization service. Among predisposing factors, only educational level was statistically significant. Individuals with higher education were less likely to report unmet needs. Among enabling factors, residential area, social support, personal yearly income and personal healthcare expenditure were strongly associated with the presence of unmet need. Those with less enabling resources (e.g. residing in rural areas) were more likely to report unmet need [Odds ratio (OR) = 1.5–6.5]. All the need factors, except for physical function, were strongly associated with the presence of unmet need. Seniors in poorer health (e.g. in fair or poor health) were more likely to report unmet need than their counterparts in better health (OR = 1.5–2.8).

Conclusions

In spite of relatively high insurance coverage rates, unmet need for hospitalization service remains high among the elderly people of Zhejiang province in China. Application of comprehensive intervention strategies such as conducting health education, creating social support, promoting community participation and promoting inter-sectional cooperation may be more effective in reducing unmet need for hospitalization service.

 
 
 
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