Funkner Anastasia, Balabaeva Ksenia, Kovalchuk Sergey
ITMO University, Saint Petersburg, Russia.
Stud Health Technol Inform. 2020 Jun 16;270:342-346. doi: 10.3233/SHTI200179.
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.
在医学领域开发预测模型需要从非结构化临床文本中提取额外特征。在俄罗斯，没有用于自然语言处理以解决医疗记录问题的工具。本文致力于否定检测模块。基于梯度提升分类器的无语料库机器学习方法用于检测文本中疾病是被否定、未提及还是被呈现。该检测器对五种疾病的否定进行分类，平均F值在0.81至0.93之间。通过预测急性冠状动脉综合征患者的手术情况，已证明了否定检测的益处。
