title: On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions.
abstract: The advances achieved in Natural Language Processing make it possible to automatically mine information from electronically created documents. Many Natural Language Processing methods that extract information from texts make use of annotated corpora, but these are scarce in the clinical domain due to legal and ethical issues. In this paper we present the creation of the IxaMed-GS gold standard composed of real electronic health records written in Spanish and manually annotated by experts in pharmacology and pharmacovigilance. The experts mainly annotated entities related to diseases and drugs, but also relationships between entities indicating adverse drug reaction events. To help the experts in the annotation task, we adapted a general corpus linguistic analyzer to the medical domain. The quality of the annotation process in the IxaMed-GS corpus has been assessed by measuring the inter-annotator agreement, which was 90.53% for entities and 82.86% for events. In addition, the corpus has been used for the automatic extraction of adverse drug reaction events using machine learning.
authors.affiliation: IXA Group, University of the Basque Country (UPV-EHU), Computer Engineering Faculty, P. Manuel Lardizabal, 1, 20018 Donostia-San Sebastián, Spain(1).
authors.forename: Maite
authors.initials: M
authors.lastname: Oronoz
authors.affiliation: IXA Group, University of the Basque Country (UPV-EHU), Computer Engineering Faculty, P. Manuel Lardizabal, 1, 20018 Donostia-San Sebastián, Spain(1).
authors.forename: Koldo
authors.initials: K
authors.lastname: Gojenola
authors.affiliation: IXA Group, University of the Basque Country (UPV-EHU), Computer Engineering Faculty, P. Manuel Lardizabal, 1, 20018 Donostia-San Sebastián, Spain(1).
authors.forename: Alicia
authors.initials: A
authors.lastname: Pérez
authors.affiliation: IXA Group, University of the Basque Country (UPV-EHU), Computer Engineering Faculty, P. Manuel Lardizabal, 1, 20018 Donostia-San Sebastián, Spain(1).
authors.forename: Arantza Díaz
authors.initials: AD
authors.lastname: de Ilarraza
authors.affiliation: IXA Group, University of the Basque Country (UPV-EHU), Computer Engineering Faculty, P. Manuel Lardizabal, 1, 20018 Donostia-San Sebastián, Spain(1). Electronic address: arantza.casillas@ehu.eus.
authors.forename: Arantza
authors.initials: A
authors.lastname: Casillas
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001331:Automation; D057225:Data Mining / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions; D057286:Electronic Health Records / Q000592:standards*; D007802:Language; D008037:Linguistics; D000069550:Machine Learning; D009323:Natural Language Processing; D004364:Pharmaceutical Preparations; D060735:Pharmacovigilance; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D014175:Translating
chemical_list: D004364:Pharmaceutical Preparations
keywords: Adverse drug reaction; Clinical text; Gold standard; Text mining