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The data retrieval optimization from the perspective of evidence-based medicine

机译：循证医学视角下的数据检索优化

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The paper is devoted to classification of MEDLINE abstracts into categories that correspond to types of medical interventions - types of patient treatments. This set of categories was extracted from Clinicaltrials.gov web site. Few classification algorithms were tested includingMultinomial Naive Bayes, Multinomial Logistic Regression, and Linear SVM implementations from sklearn machine learning library. Document marking was based on the consideration of abstracts containing links to the Clinicaltrials.gov Web site. As the result of an automatical marking 3534 abstracts were marked for training and testing the set of algorithms metioned above. Best result of multinomial classification was achieved by Linear SVM with macro evaluation precision 70.06%, recall 55.62% and F-measure 62.01%, and micro evaluation precision 64.91%, recall 79.13% and F-measure 71.32%.
机译：本文致力于将MEDLINE摘要分类为与医疗干预类型（患者治疗类型）相对应的类别。这组类别是从Clinicaltrials.gov网站上提取的。很少有分类算法经过测试，包括来自sklearn机器学习库的多项式朴素贝叶斯，多项式逻辑回归和线性SVM实现。文档标记是基于对包含指向Clinicaltrials.gov网站的链接的摘要的考虑。自动标记的结果是，对3534个摘要进行了标记，以训练和测试上述算法集。线性分类支持向量机获得多项式分类的最佳结果，其宏评估精度为70.06％，召回率为55.62％和F-measure为62.01％，微观评估精度为64.91％，召回率是79.13％和F-measure为71.32％。

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