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ObjectiveVascular endothelial dysfunction is considered an early predictor of endothelial injury and the initiating factor of atherosclerosis (AS). Brachial artery flow-mediated dilation (FMD) can detect endothelial injury early and provide important prognostic information beyond traditional cardiovascular (CV) risk factors. This study aimed to find the influencing factors of FMD and develop a simple prediction model in populations with different body mass indices (BMIs).MethodsIn total, 420 volunteers with different BMIs were recruited in our study. Subjects were randomly assigned to the derivation and validation cohorts (the ratio of the two was 1:2) with simple random sampling. The former was used for influencing factors searching and model construction of FMD and the latter was used for verification and performance evaluation.ResultsThe population was divided into two groups, i.e., 140 people in the derivation group and 280 people in the verification group. Analyzing in the training data, we found that females had higher FMD than males (p < 0.05), and FMD decreased with age (p < 0.05). In people with diabetes, hypertension or obesity, FMD was lower than that in normal individuals (p < 0.05). Through correlation analysis and linear regression, we found the main influencing factors of FMD: BMI, age, waist-to-hip radio (WHR), aspartate aminotransferase (AST) and low-density lipoprotein (LDL). And we developed a simple FMD prediction model: FMD = −0.096BMI−0.069age−4.551WHR−0.015AST−0.242LDL+17.938, where R2 = 0.599, and adjusted R2 = 0.583. There was no statistically significant difference between the actual FMD and the predicted FMD in the verification group (p > 0.05). The intra-class correlation coefficient (ICC) was 0.77. In a Bland-Altman plot, the actual FMD and the predicted FMD also showed good agreement. This prediction model had good hints in CV risk stratification (area under curve [AUC]: 0.780, 95 % confidence intervals [95% CI]: 0.708–0.852, p < 0.001), with a sensitivity and specificity of 73.8 and 72.1%, respectively.ConclusionsMales, older, obesity, hypertension, diabetes, smoking, etc. were risk factors for FMD, which was closely related to CV disease (CVD). We developed a simple equation to predict FMD, which showed good agreement between the training and validation groups. And it would greatly simplify clinical work and may help physicians follow up the condition and monitor therapeutic effect. But further validation and modification bears great significance.

In information extraction, a central and challenging task is extraction of relations. Systems that extract relations from text tend to be very productive, so it is important to quantify confidence or certainty in what is extracted. In this thesis we introduce a framework to assess confidence in relation extraction systems. We trained our system using a logistic regression model based on manually tagged sentences from the New York Times Annotated Corpora. Empirical results based on ROC curves show that our system performs better at computing confidence than previous systems such as Reverb. We conclude with a detailed analysis of the features used in our system and explain how these features might be tailored for use in other relation extraction systems.