Cadec: A corpus of adverse drug event annotations

• Published in: Journal of biomedical informatics Vol. 55; pp. 73 - 81
• Main Authors: Karimi, Sarvnaz, Metke-Jimenez, Alejandro, Kemp, Madonna, Wang, Chen
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2015
• Subjects: Adverse drug reaction; Adverse Drug Reaction Reporting Systems - organization & administration; Annotated corpus; Annotations; Consumer Health Information - organization & administration; Consumer reviews; Data Mining - methods; Datasets as Topic - statistics & numerical data; Digital media; Drug safety; Drug-Related Side Effects and Adverse Reactions - classification; Drug-Related Side Effects and Adverse Reactions - epidemiology; Drugs; Grammars; Guidelines; Guidelines as Topic; Humans; Information extraction; Information retrieval; Machine Learning; MedDRA; Medical forum; Natural Language Processing; SNOMED CT; Social media; Social Media - classification; Social Media - organization & administration; Social networks; Terminology as Topic; Texts; Vocabulary, Controlled; SNOMED CT; Consumer reviews; MedDRA; Social media; Medical forum; Annotated corpus; Information extraction; Adverse drug reaction; Drug safety
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2015.03.010

[Display omitted] •Introduction of CADEC an annotated corpus of consumer reviews in pharmacovigilance.•A review and comparison of available relevant resources.•Challenges and lessons from the process of creating such resources. CSIRO Adverse Drug Event Corpus (Cadec) is a new rich annotated corpus of medical forum posts on patient-reported Adverse Drug Events (ADEs). The corpus is sourced from posts on social media, and contains text that is largely written in colloquial language and often deviates from formal English grammar and punctuation rules. Annotations contain mentions of concepts such as drugs, adverse effects, symptoms, and diseases linked to their corresponding concepts in controlled vocabularies, i.e., SNOMED Clinical Terms and MedDRA. The quality of the annotations is ensured by annotation guidelines, multi-stage annotations, measuring inter-annotator agreement, and final review of the annotations by a clinical terminologist. This corpus is useful for studies in the area of information extraction, or more generally text mining, from social media to detect possible adverse drug reactions from direct patient reports. The corpus is publicly available at https://data.csiro.au.1The data can be used for research purposes only, under the CSIRO data licence.1