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Empirical advances with text mining of electronic health records

Overview of attention for article published in BMC Medical Informatics and Decision Making, August 2017
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Title
Empirical advances with text mining of electronic health records
Published in
BMC Medical Informatics and Decision Making, August 2017
DOI 10.1186/s12911-017-0519-0
Pubmed ID
Authors

T. Delespierre, P. Denormandie, A. Bar-Hen, L. Josseran

Abstract

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. 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. 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. 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 138 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 12%
Researcher 15 11%
Student > Ph. D. Student 14 10%
Student > Bachelor 9 7%
Professor 6 4%
Other 32 23%
Unknown 46 33%
Readers by discipline Count As %
Computer Science 26 19%
Medicine and Dentistry 19 14%
Nursing and Health Professions 8 6%
Agricultural and Biological Sciences 7 5%
Business, Management and Accounting 4 3%
Other 18 13%
Unknown 56 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 August 2017.
All research outputs
#18,569,430
of 22,999,744 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,582
of 2,006 outputs
Outputs of similar age
#243,397
of 317,366 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#22
of 30 outputs
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So far Altmetric has tracked 2,006 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.