论文标题
俄罗斯临床文本挖掘的否定检测
Negation Detection for Clinical Text Mining in Russian
论文作者
论文摘要
在医学中开发预测性建模需要非结构化临床文本的其他特征。在俄罗斯，没有用于应对病历问题的自然语言处理工具。本文专门用于否定检测模块。无语料库的机器学习方法基于梯度提升分类器用于检测文本中是否拒绝，未提及或没有提出疾病。检测器对五种疾病进行否定分类，并显示从0.81至0.93的平均F评分。通过预测急性冠状动脉综合征患者的手术的存在来证明否定检测的好处。
Developing predictive modeling in medicine requires additional features from unstructured clinical texts. In Russia, there are no instruments for natural language processing to cope with problems of medical records. This paper is devoted to a module of negation detection. The corpus-free machine learning method is based on gradient boosting classifier is used to detect whether a disease is denied, not mentioned or presented in the text. The detector classifies negations for five diseases and shows average F-score from 0.81 to 0.93. The benefits of negation detection have been demonstrated by predicting the presence of surgery for patients with the acute coronary syndrome.
