title: Automatic acquisition of synonym resources and assessment of their impact on the enhanced search in EHRs.
abstract: 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.
authors.affiliation: Centre de Recherche des Cordeliers, 75006 Paris, France. natalia.grabar@spim.jussieu.fr
authors.forename: Natalia
authors.initials: N
authors.lastname: Grabar
authors.forename: Paul-Christophe
authors.initials: PC
authors.lastname: Varoutas
authors.forename: Philippe
authors.initials: P
authors.lastname: Rizand
authors.forename: Alain
authors.initials: A
authors.lastname: Livartowski
authors.forename: Thierry
authors.initials: T
authors.lastname: Hamon
mesh_terms: D005602:France; D006751:Hospital Information Systems / Q000458:organization & administration*; D006801:Humans; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D009626:Terminology as Topic