Cadec: A corpus of adverse drug event annotations
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TLDR
A new rich annotated corpus of medical forum posts on patient-reported Adverse Drug Events (ADEs), which contains text that is largely written in colloquial language and often deviates from formal English grammar and punctuation rules.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2015-06-01 and is currently open access. It has received 217 citations till now. The article focuses on the topics: SNOMED CT.read more
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Information retrieval and text mining technologies for chemistry
Martin Krallinger,Obdulia Rabal,Anália Lourenço,Anália Lourenço,Julen Oyarzabal,Alfonso Valencia +5 more
TL;DR: This Review provides a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting information demands of chemical information contained in scientific literature, patents, technical reports, or the web.
Proceedings ArticleDOI
Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation
Nut Limsopatham,Nigel Collier +1 more
TL;DR: This work investigates the use of neural networks to learn the transition between layman’s language used in social media messages and formal medicallanguage used in the descriptions of medical concepts in a standard ontology, and proposes approaches to outperform existing effective baselines.
Proceedings ArticleDOI
An Analysis of Simple Data Augmentation for Named Entity Recognition
Xiang Dai,Heike Adel +1 more
TL;DR: It is shown that simple augmentation can boost performance for both recurrent and transformer-based models, especially for small training sets, through experiments on two data sets from the biomedical and materials science domains.
Proceedings ArticleDOI
Adverse Drug Event Detection in Tweets with Semi-Supervised Convolutional Neural Networks
Kathy Lee,Ashequl Qadir,Sadid A. Hasan,Vivek V. Datla,Aaditya Prakash,Joey Liu,Oladimeji Farri +6 more
TL;DR: This work builds several semi-supervised convolutional neural network models for ADE classification in tweets, specifically leveraging different types of unlabeled data in developing the models to address the problem.
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Systematic review on the prevalence, frequency and comparative value of adverse events data in social media
TL;DR: There was general agreement that a higher frequency of adverse events was found in social media and that this was particularly true for 'symptom' related and 'mild' adverse events.
References
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DrugBank: a comprehensive resource for in silico drug discovery and exploration
David S. Wishart,Craig Knox,An Chi Guo,Savita Shrivastava,Murtaza Hassanali,Paul Stothard,Zhan Chang,Jennifer Woolsey +7 more
TL;DR: DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug data with comprehensive drug target information and is fully searchable supporting extensive text, sequence, chemical structure and relational query searches.
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brat: a Web-based Tool for NLP-Assisted Text Annotation
TL;DR: The brat rapid annotation tool (BRAT) is introduced, an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology and an evaluation of annotation assisted by semantic class disambiguation on a multicategory entity mention annotation task, showing a 15% decrease in total annotation time.
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PubTator: a web-based text mining tool for assisting biocuration
TL;DR: PubTator is described, a web-based system for assisting biocuration that featuring a PubMed-like interface, and being equipped with multiple challenge-winning text mining algorithms to ensure the quality of its automatic results.
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Special Report: NCBI disease corpus: A resource for disease name recognition and concept normalization
TL;DR: The results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.
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The DDI corpus
TL;DR: A manually annotated corpus consisting of 792 texts selected from the DrugBank database and other 233 Medline abstracts, annotated with a total of 18,502 pharmacological substances and 5028 DDIs, including both PK as well as PD interactions, shows that the corpus has enough quality to be used for training and testing NLP techniques applied to the field of Pharmacovigilance.