Negation Detection for Clinical Text Mining in Russian
This work addresses a gap in clinical text mining for Russian medical records, enabling better predictive modeling in medicine, though it is incremental as it applies existing methods to a new language domain.
The paper tackled the problem of negation detection in Russian clinical texts, a missing tool in medical NLP for Russia, by developing a corpus-free machine learning method using a gradient boosting classifier to detect if diseases are denied, not mentioned, or present, achieving average F-scores from 0.81 to 0.93 for five diseases.
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.