Question

求高手翻译。

这只是其中一部分，拒绝软件翻译，分数不是问题，加好友，长期合作。
European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints classify Candida
strains with a fluconazole MIC < 2 mg/liter as susceptible, those with a fluconazole MIC of 4 mg/liter as
representing intermediate susceptibility, and those with a fluconazole MIC > 4 mg/liter as resistant. Machine
learning models are supported by complex statistical analyses assessing whether the results have statistical
relevance. The aim of this work was to use supervised classification algorithms to analyze the clinical data used
to produce EUCAST fluconazole breakpoints. Five supervised classifiers (J48, Correlation and Regression
Trees [CART], OneR, Naïve Bayes, and Simple Logistic) were used to analyze two cohorts of patients with
oropharyngeal candidosis and candidemia. The target variable was the outcome of the infections, and the
predictor variables consisted of values for the MIC or the proportion between the dose administered and the
MIC of the isolate (dose/MIC). Statistical power was assessed by determining values for sensitivity and
specificity, the false-positive rate, the area under the receiver operating characteristic (ROC) curve, and the
Matthews correlation coefficient (MCC). CART obtained the best statistical power for a MIC > 4 mg/liter for
detecting failures (sensitivity, 87%; false-positive rate, 8%; area under the ROC curve, 0.89; MCC index, 0.80).
For dose/MIC determinations, the target was >75, with a sensitivity of 91%, a false-positive rate of 10%, an
area under the ROC curve of 0.90, and an MCC index of 0.80. Other classifiers gave similar breakpoints with
lower statistical power. EUCAST fluconazole breakpoints have been validated by means of machine learning
methods. These computer tools must be incorporated in the process for developing breakpoints to avoid

Answer 1

欧洲的抗生素药物敏感试验委员会(即EUCAST)对念珠菌做出如下断点分类：
含氟康唑最低抑菌浓度(MIC)<2毫克/升(mg/liter)的为极敏感性；
其最低抑菌浓度为2mg/liter<MIC<4mg/liter的为中度敏感性；
而MIC>4mg/liter的一类为抗感性。
机器实验模型结果由复杂的统计分析完成，以评估其结果之间是否有统计关联。

此研究旨在利用监督分类方法来分析一些产生氟康唑浓度临界值的临床数据。研究用到5种监督分类的方式(J48, CART(即相关与回归分析树形图)，OneR法，贝叶斯...法, 简单曲线法)**[1]来对比分析两组分别患有口咽念珠菌病和念珠菌血症的患者。目标变量为患者感染的结果；预测变量由各组实验的MIC值，或dose/MIC而组成（dose/MIC指的是：投入剂量与分离的MIC之比值）。

检验效能可以通过如下数据的确定而得出：敏感性与特异性的值，假阳性率，ROC曲线下区域面积(ROC曲线即为被试者操作特征曲线)，以及MCC(即马修斯相关系数)。采用CART方法可以得到在MIC>4 mg/liter 检测失效下的最佳统计效能（敏感性87％，假阳性率8％，ROC曲线下面积0.89;MCC指数为0.80）。而dose/MIC所确定目标变量值是>75，敏感度为91％，假阳性率为10％，ROC曲线下面积为0.90，MCC指数为0.80。
其他分类统计法也得出了相似的临界点，但统计功效显著性欠佳。

欧洲EUCAST机构所得出的氟康唑断点已经有效验证了利用机器进行实验学习的方法。在计算临界点的过程中，必须引进此类计算机工具以避免。。。。。。(没了)

[1]这个这个。。。括号里说的是什么东东，介个我真不认识了，反正是5种。但是我想你是应该认识的。(*^__^*)