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 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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PID https://www.doi.org/10.1186/s12911-017-0519-0
PID pmc:PMC5568397
PID pmid:28830417
URL https://rd.springer.com/article/10.1186/s12911-017-0519-0
URL https://doi.org/10.1186/s12911-017-0519-0
URL https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-017-0519-0
URL https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-017-0519-0
URL https://academic.microsoft.com/#/detail/2745560856
URL https://link.springer.com/article/10.1186/s12911-017-0519-0
URL http://link.springer.com/content/pdf/10.1186/s12911-017-0519-0.pdf
URL https://dx.doi.org/10.1186/s12911-017-0519-0
URL http://europepmc.org/articles/PMC5568397
URL https://www.ncbi.nlm.nih.gov/pubmed/28830417
URL http://dx.doi.org/10.1186/s12911-017-0519-0
URL https://dblp.uni-trier.de/db/journals/midm/midm17.html#DelespierreDBJ17
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Author Delespierre, T., 0000-0002-6433-6959
Author Denormandie, P.
Author Bar-Hen, A.
Author Josseran, L.
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Collected From Europe PubMed Central; PubMed Central; ORCID; UnpayWall; Datacite; Crossref; Microsoft Academic Graph
Hosted By Europe PubMed Central; BMC Medical Informatics and Decision Making
Journal BMC Medical Informatics and Decision Making, 17,
Publication Date 2017-08-22
Publisher BioMed Central
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Language English
Resource Type Other literature type; Article; UNKNOWN
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::bfed1a65996d0b3e78b9632a0cfa1c19
Author jsonws_user
Last Updated 25 December 2020, 15:11 (CET)
Created 25 December 2020, 15:11 (CET)