俄语临床文本挖掘中的否定检测
Negation Detection for Clinical Text Mining in Russian
摘要：
在医学中开发预测建模需要来自非结构化临床文本的额外特征。在俄罗斯，没有自然语言处理的工具来处理医疗记录的问题。本文研究的是一个否定检测模块。无语料库机器学习方法是基于梯度提升分类器，用于检测一种疾病是否被否认，没有提到或在文本中提出。检测器对五种疾病的否定进行分类，并显示平均F分数从0.81到0.93。否定检测的益处已经通过预测急性冠状动脉综合征患者的手术存在而得到证实。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
