Knowledge acquisition for computation of semantic distance between WHO-ART terms.
Computation of semantic distance between adverse drug reactions terms may be an efficient way to group related medical conditions in pharmacovigilance case reports. Previous experience with ICD-10 on a semantic distance tool highlighted a bottleneck related to manual description of formal definitions in large terminologies. We propose a method based on acquisition of formal definitions by knowledge extraction from UMLS and morphosemantic analysis. These formal definitions are expressed with SNOMED International terms. We provide formal definitions for 758 WHO-ART terms: 321 terms defined from UMLS, 320 terms defined using morphosemantic analysis and 117 terms defined after expert evaluation. Computation of semantic distance (e.g. k-nearest neighbours) was implemented in J2EE terminology services. Similar WHO-ART terms defined by automated knowledge acquisition and ICD terms defined manually show similar behaviour in the semantic distance tool. Our knowledge acquisition method can help us to generate new formal definitions of medical terms for our semantic distance terminology services.
Iavindrasana
Jimison
J
INSERM U729, Université Paris Descartes, Faculté de Médecine, Paris, France.
Bousquet
Cédric
C
Jaulent
Marie-Christine
MC
D016907:Adverse Drug Reaction Reporting Systems; D001185:Artificial Intelligence; D005602:France; D009323:Natural Language Processing; D012660:Semantics; D009626:Terminology as Topic; D017432:Unified Medical Language System; D014944:World Health Organization