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Negation Detection for Clinical Text Mining in Russian.

https://arctichealth.org/en/permalink/ahliterature305542
Source
Stud Health Technol Inform. 2020 Jun 16; 270:342-346
Publication Type
Journal Article
Date
Jun-16-2020
Author
Anastasia Funkner
Ksenia Balabaeva
Sergey Kovalchuk
Author Affiliation
ITMO University, Saint Petersburg, Russia.
Source
Stud Health Technol Inform. 2020 Jun 16; 270:342-346
Date
Jun-16-2020
Language
English
Publication Type
Journal Article
Keywords
Data Mining
Electronic Health Records
Language
Machine Learning
Natural Language Processing
Russia
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.
PubMed ID
32570403 View in PubMed
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Automated Spelling Correction for Clinical Text Mining in Russian.

https://arctichealth.org/en/permalink/ahliterature305544
Source
Stud Health Technol Inform. 2020 Jun 16; 270:43-47
Publication Type
Journal Article
Date
Jun-16-2020
Author
Ksenia Balabaeva
Anastasia Funkner
Sergey Kovalchuk
Author Affiliation
ITMO University, Saint Petersburg, Russia.
Source
Stud Health Technol Inform. 2020 Jun 16; 270:43-47
Date
Jun-16-2020
Language
English
Publication Type
Journal Article
Keywords
Algorithms
Data Mining
Electronic Health Records
Language
Machine Learning
Natural Language Processing
Russia
Abstract
The main goal of this paper is to develop a spell checker module for clinical text in Russian. The described approach combines string distance measure algorithms with technics of machine learning embedding methods. Our overall precision is 0.86, lexical precision - 0.975 and error precision is 0.74. We develop spell checker as a part of medical text mining tool regarding the problems of misspelling, negation, experiencer and temporality detection.
PubMed ID
32570343 View in PubMed
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