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Empirical advances with text mining of electronic health records

BACKGROUND: Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents’ data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a...

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Autores principales: Delespierre, T., Denormandie, P., Bar-Hen, A., Josseran, L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5568397/
https://www.ncbi.nlm.nih.gov/pubmed/28830417
http://dx.doi.org/10.1186/s12911-017-0519-0
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author Delespierre, T.
Denormandie, P.
Bar-Hen, A.
Josseran, L.
author_facet Delespierre, T.
Denormandie, P.
Bar-Hen, A.
Josseran, L.
author_sort Delespierre, T.
collection PubMed
description BACKGROUND: Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents’ data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents’ care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents’ care and lives. METHODS: Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents’ sample as well as on other health data using a health model measuring the residents’ care level needs. RESULTS: By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents’ health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents’ data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives. CONCLUSIONS: This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents’ health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and helping to integrate the EHR system into the health staff work environment. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-017-0519-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-55683972017-08-29 Empirical advances with text mining of electronic health records Delespierre, T. Denormandie, P. Bar-Hen, A. Josseran, L. BMC Med Inform Decis Mak Research Article BACKGROUND: Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents’ data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents’ care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents’ care and lives. METHODS: Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents’ sample as well as on other health data using a health model measuring the residents’ care level needs. RESULTS: By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents’ health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents’ data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives. CONCLUSIONS: This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents’ health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and helping to integrate the EHR system into the health staff work environment. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-017-0519-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-08-22 /pmc/articles/PMC5568397/ /pubmed/28830417 http://dx.doi.org/10.1186/s12911-017-0519-0 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Delespierre, T.
Denormandie, P.
Bar-Hen, A.
Josseran, L.
Empirical advances with text mining of electronic health records
title Empirical advances with text mining of electronic health records
title_full Empirical advances with text mining of electronic health records
title_fullStr Empirical advances with text mining of electronic health records
title_full_unstemmed Empirical advances with text mining of electronic health records
title_short Empirical advances with text mining of electronic health records
title_sort empirical advances with text mining of electronic health records
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5568397/
https://www.ncbi.nlm.nih.gov/pubmed/28830417
http://dx.doi.org/10.1186/s12911-017-0519-0
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