Literature DB >> 25817970

Cadec: A corpus of adverse drug event annotations.

Sarvnaz Karimi1, Alejandro Metke-Jimenez2, Madonna Kemp2, Chen Wang2.   

Abstract

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.(1).
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Adverse drug reaction; Annotated corpus; Consumer reviews; Drug safety; Information extraction; MedDRA; Medical forum; SNOMED CT; Social media

Mesh:

Year:  2015        PMID: 25817970     DOI: 10.1016/j.jbi.2015.03.010

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  21 in total

1.  Ensemble method-based extraction of medication and related information from clinical texts.

Authors:  Youngjun Kim; Stéphane M Meystre
Journal:  J Am Med Inform Assoc       Date:  2020-01-01       Impact factor: 4.497

2.  RadLex Normalization in Radiology Reports.

Authors:  Surabhi Datta; Jordan Godfrey-Stovall; Kirk Roberts
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

3.  The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task on clinical concept normalization for clinical records.

Authors:  Sam Henry; Yanshan Wang; Feichen Shen; Ozlem Uzuner
Journal:  J Am Med Inform Assoc       Date:  2020-10-01       Impact factor: 4.497

Review 4.  Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.

Authors:  G Gonzalez-Hernandez; A Sarker; K O'Connor; G Savova
Journal:  Yearb Med Inform       Date:  2017-09-11

5.  Semantic Search for Large Scale Clinical Ontologies.

Authors:  Duy-Hoa Ngo; Madonna Kemp; Donna Truran; Bevan Koopman; Alejandro Metke-Jimenez
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

6.  A simple neural vector space model for medical concept normalization using concept embeddings.

Authors:  Dongfang Xu; Timothy Miller
Journal:  J Biomed Inform       Date:  2022-04-23       Impact factor: 8.000

7.  Automated gathering of real-world data from online patient forums can complement pharmacovigilance for rare cancers.

Authors:  Anne Dirkson; Suzan Verberne; Wessel Kraaij; Gerard van Oortmerssen; Hans Gelderblom
Journal:  Sci Rep       Date:  2022-06-20       Impact factor: 4.996

8.  An unsupervised and customizable misspelling generator for mining noisy health-related text sources.

Authors:  Abeed Sarker; Graciela Gonzalez-Hernandez
Journal:  J Biomed Inform       Date:  2018-11-13       Impact factor: 6.317

Review 9.  Systematic review on the prevalence, frequency and comparative value of adverse events data in social media.

Authors:  Su Golder; Gill Norman; Yoon K Loke
Journal:  Br J Clin Pharmacol       Date:  2015-09-16       Impact factor: 4.335

10.  Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study.

Authors:  Majid Rastegar-Mojarad; Hongfang Liu; Priya Nambisan
Journal:  JMIR Res Protoc       Date:  2016-06-16
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