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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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