Work

Negation Detection for Clinical Text Mining in Russian

Year: 2020

Type: book-chapter

Abstract: 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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti200179

Authors: Funkner Anastasia, Balabaeva Ksenia, Kovalchuk Sergey

Institution: ITMO University

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: Russian Science Foundation