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Natural Language Processing Based Instrument for Classification of Free Text Medical Records

According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work in...

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Autores principales: Khachidze, Manana, Tsintsadze, Magda, Archuadze, Maia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030470/
https://www.ncbi.nlm.nih.gov/pubmed/27668260
http://dx.doi.org/10.1155/2016/8313454
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author Khachidze, Manana
Tsintsadze, Magda
Archuadze, Maia
author_facet Khachidze, Manana
Tsintsadze, Magda
Archuadze, Maia
author_sort Khachidze, Manana
collection PubMed
description According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a “shrink” method, based on features selection, was introduced and applied. At the first stage of classification the results of the “shrink” case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful.
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spelling pubmed-50304702016-09-25 Natural Language Processing Based Instrument for Classification of Free Text Medical Records Khachidze, Manana Tsintsadze, Magda Archuadze, Maia Biomed Res Int Research Article According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a “shrink” method, based on features selection, was introduced and applied. At the first stage of classification the results of the “shrink” case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful. Hindawi Publishing Corporation 2016 2016-09-07 /pmc/articles/PMC5030470/ /pubmed/27668260 http://dx.doi.org/10.1155/2016/8313454 Text en Copyright © 2016 Manana Khachidze et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Khachidze, Manana
Tsintsadze, Magda
Archuadze, Maia
Natural Language Processing Based Instrument for Classification of Free Text Medical Records
title Natural Language Processing Based Instrument for Classification of Free Text Medical Records
title_full Natural Language Processing Based Instrument for Classification of Free Text Medical Records
title_fullStr Natural Language Processing Based Instrument for Classification of Free Text Medical Records
title_full_unstemmed Natural Language Processing Based Instrument for Classification of Free Text Medical Records
title_short Natural Language Processing Based Instrument for Classification of Free Text Medical Records
title_sort natural language processing based instrument for classification of free text medical records
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030470/
https://www.ncbi.nlm.nih.gov/pubmed/27668260
http://dx.doi.org/10.1155/2016/8313454
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