Automatic annotation of ICD-to-MedDRA mappings with SKOS predicates

Gunnar Declerck; Julien Souvignet; Jean-Marie Rodrigues; Marie-Christine Jaulent

Automatic annotation of ICD-to-MedDRA mappings with SKOS predicates

Stud Health Technol Inform. 2014:205:1013-7.

Authors

Gunnar Declerck¹, Julien Souvignet¹, Jean-Marie Rodrigues¹, Marie-Christine Jaulent¹

Affiliation

¹ INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS, (UMR_S 1142), F-93430, Villetaneuse, France.

PMID: 25160341

Abstract

Robust alignments between ICD and MedDRA are essential to enable the secondary use of clinical data for pharmacovigilance research. UMLS makes available ICD-to-MedDRA mappings, but they are only poorly specified, which introduces difficulties when exploited in an automatic way. SKOS vocabulary can help achieve quality and machine-processable mappings. We have developed an algorithm based on several simple rules which annotates automatically ICD-to-MedDRA mappings with SKOS predicates. The method was tested and evaluated on a sample of ICD-10-to MedDRA mappings extracted from UMLS. The algorithm demonstrated satisfying performances, especially for skos:exactMatch properties, which suggests that automatic methods can be used to improve the quality of terminology mappings.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adverse Drug Reaction Reporting Systems / organization & administration*
Algorithms
Artificial Intelligence
Dictionaries, Pharmaceutic as Topic*
Documentation / standards
Guidelines as Topic*
International Classification of Diseases / standards*
Natural Language Processing*
Pharmacovigilance
Semantics
Terminology as Topic*
Vocabulary, Controlled*