|
 
|
[Bar17b] Empirical advances with text mining of electronic health records
Revue Internationale avec comité de lecture : Journal BMC medical informatics and decision making, vol. 17(1), pp. 127, 2017, (doi:https://doi.org/10.1186/s12911-017-0519-0)Mots clés: Nursing homesSQL queryInformation extractionNamed entity recognitionData miningText miningWord cloudMultiple component analysisPrincipal component analysisHierarchical clustering
Résumé:
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.
Commentaires:
T. Delespierre, P. Denormandie, A. Bar-Hen and L. Josseran
BibTeX
| @article { | |||
| Bar17b, | |||
| title | = | "{Empirical advances with text mining of electronic health records}", | |
| author | = | "A. Bar-Hen", | |
| journal | = | "BMC medical informatics and decision making", | |
| year | = | 2017, | |
| volume | = | 17, | |
| number | = | 1, | |
| pages | = | "127", | |
| note | = | "{T. Delespierre, P. Denormandie, A. Bar-Hen and L. Josseran}", | |
| doi | = | "https://doi.org/10.1186/s12911-017-0519-0", | |
| } | |||
