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Methods Inf Med 2009; 48(02): 149-154
DOI: 10.3414/ME9213
Original Articles
Schattauer GmbH

Automatic Acquisition of Synonym Resources and Assessment of their Impact on the Enhanced Search in EHRs

N. Grabar
1   Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
2   INSERM, U872, Paris, France
3   HEGP AP-HP, Paris, France
,
P.-C. Varoutas
4   Institut Curie, Département d’Information Médicale, Paris, France
,
P. Rizand
4   Institut Curie, Département d’Information Médicale, Paris, France
,
A. Livartowski
4   Institut Curie, Département d’Information Médicale, Paris, France
,
T. Hamon
5   LIPN-UMR 7030, Université Paris-Nord – CNRS, Villetaneuse, France
› Author Affiliations
Further Information

Publication History

18 February 2009

Publication Date:
17 January 2018 (online)

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Summary

Objective: Currently, the use of natural language processing (NLP) approaches in order to improve search and exploration of electronic health records (EHRs) within healthcare information systems is not a common practice. One reason for this is the lack of suitable lexical resources. Indeed, in order to support such tasks, various types of such resources need to be collected or acquired (i.e., morphological, orthographic, synonymous).

Methods: We propose a novel method for the acquisition of synonymy resources. This method is language-independent and relies on existence of structured terminologies. It enables to decipher hidden synonymy relations between simple words and terms on the basis of their syntactic analysis and exploitation of their compositionality.

Results: Applied to series of synonym terms from the French subset of the UMLS, the method shows 99% precision. The overlap between thus inferred terms and the existing sparse resources of synonyms is very low. In order to better integrate these resources in an EHR search system, we analyzed a sample of clinical queries submitted by healthcare professionals.

Conclusions: Observation of clinical queries shows that they make a very little use of the query expansion function, and, whenever they do, synonymy relations are rarely involved.

Keywords

Natural language processing - artificial intelligence - terminology - hospital information systems - France
 

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