Objective: The aim of this paper is to propose an extended translation of the MeSH thesaurus based on Wikipedia pages. Methods: A mapping was realized between each MeSH descriptor (preferred terms and synonyms) and corresponding Wikipedia pages. Results: A tool called “WikiMeSH” has been developed. Among the top 20 languages of this study, seven have currently no MeSH translations: Arabic, Catalan, Farsi (Iran), Mandarin Chinese, Korean, Serbian, and Ukrainian. For these seven languages, WikiMeSH is proposing a translation for 47% for Arabic to 34% for Serbian. Conclusion: WikiMeSH is an interesting tool to translate the MeSH thesaurus and other health terminologies and ontologies based on a mapping to Wikipedia pages.
Background: Unstructured data from electronic health record is a gold mine. Doc’EDS is a pre-screening tool based on textual and semantic analysis. The system provides an easy-to-use interface to search documents in French. The aim of this study is to present the tools and to provide a formal evaluation of its semantic features. Material & Methods: Doc’EDS is a search tool built on the top of the clinical data warehouse developed in the Rouen University Hospital. This tool is a multilevel search engine combining structured and unstructured data. It also provides basic analytics features and semantic utilities. A formal evaluation has been conducted to measure the implemented Natural Language Processing algorithms. Results: About 17,3 million of narrative documents are contained in this CDW. The formal evaluation has been conducted over 5,000 clinical concepts that were manually collected. Negation concepts detection F-measure was 0.89, hypothesis concept detection F-measure was 0.57. Conclusion: We hereby present Doc’EDS, a semantic search tool which deals with language subtleties to enhance an advanced full text search engine dedicated to French health documents. This tool is currently used on a daily basis to help researchers identifying patients thanks to unstructured data.
Background
Unstructured data from electronic health records represent a wealth of information. Doc’EDS is a pre-screening tool based on textual and semantic analysis. The Doc’EDS system provides a graphic user interface to search documents in French. The aim of this study was to present the Doc’EDS tool and to provide a formal evaluation of its semantic features.
Methods
Doc’EDS is a search tool built on top of the clinical data warehouse developed at Rouen University Hospital. This tool is a multilevel search engine combining structured and unstructured data. It also provides basic analytical features and semantic utilities. A formal evaluation was conducted to measure the impact of Natural Language Processing algorithms.
Results
Approximately 18.1 million narrative documents are stored in Doc’EDS. The formal evaluation was conducted in 5000 clinical concepts that were manually collected. The F-measures of negative concepts and hypothetical concepts were respectively 0.89 and 0.57.
Conclusion
In this formal evaluation, we have shown that Doc’EDS is able to deal with language subtleties to enhance an advanced full text search in French health documents. The Doc’EDS tool is currently used on a daily basis to help researchers to identify patient cohorts thanks to unstructured data.
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