The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships

Mulligen, Erik M. van; Fourrier‐Réglat, Annie; Genevieve, David; Molokhia, Mariam; Nieto, Ainhoa; Trifirò, Gianluca; Kors, Jan A.; Furlong, Laura I.

doi:10.1016/j.jbi.2012.04.004

Cited by 111 publications

(84 citation statements)

References 9 publications

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“…The EU-ADR corpus [16] is an annotated corpus of 300 Medline abstracts where drugs, diseases, targets, and their relationships are marked. The annotation process was performed by three annotators and divided into two steps: (i) a named-entity recognition system produced a first annotation and (ii) annotators revised this annotation using a web-based interface.…”

Section: Related Workmentioning

confidence: 99%

“…All the aforementioned corpora with adverse drug reactions annotated are gathered from Medline [16,17] or from comments on social media [19,23]. Also, they are all English-language corpora: for Spanish we have only notice of a corpus of user comments annotated with drugs and their effects [23].…”

Section: Related Workmentioning

confidence: 99%

“…Any general-purpose tool or text editor can be used as an annotation tool, but tools specifically designed to support corpus annotation provide more sophisticated functions. We examined several options: WordFreak 7 [32], Callisto 8 , iAnnotate 9 [33], GATE 10 [34], UIMA [35], the tool created for the annotation of the EU-ADR corpus [16] and Knowtator 11 [36]. For detailed information about annotation tools for biomedical literature see [37].…”

Section: Definition and Annotation Guidelinesmentioning

confidence: 99%

“…Comparing these results to other medical annotation efforts, Van Mulligen et al [16] presents the development of the EU-ADR corpus, where drugs, diseases and their relationships were annotated. Their inter-annotator agreement rate is lower than ours for both entities (they present an average of 75.3% compared to 90.5% in our case) and events (74.7% and 82.8%, respectively).…”

Section: Corpusmentioning

confidence: 99%

See 3 more Smart Citations

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions

Oronoz¹,

Gojenola²,

Pérez³

et al. 2015

Journal of Biomedical Informatics

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Definition and Annotation Guidelinesmentioning

confidence: 99%

Section: Corpusmentioning

confidence: 99%

See 2 more Smart Citations

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions

Oronoz¹,

Gojenola²,

Pérez³

et al. 2015

Journal of Biomedical Informatics

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show abstract

“…They developed two variants of CRF both modeled relation extraction task as sequence labeling task. Recently Bravo et al (2015) proposed a system for identifying association between drug disease and target in EU-ADR dataset (van Mulligen et al, 2012) and named it BeeFree. BeeFree usese combination of shallow linguistic kernel and dependency kernel for identifying relations.…”

Section: Introductionmentioning

confidence: 99%

Relation extraction from clinical texts using domain invariant convolutional neural network

Sahu¹,

Anand²,

Oruganty³

et al. 2016

Proceedings of the 15th Workshop on Biomedical Natural Language Processing

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In recent years extracting relevant information from biomedical and clinical texts such as research articles, discharge summaries, or electronic health records have been a subject of many research efforts and shared challenges. Relation extraction is the process of detecting and classifying the semantic relation among entities in a given piece of texts. Existing models for this task in biomedical domain use either manually engineered features or kernel methods to create feature vector. These features are then fed to classifier for the prediction of the correct class. It turns out that the results of these methods are highly dependent on quality of user designed features and also suffer from curse of dimensionality. In this work we focus on extracting relations from clinical discharge summaries. Our main objective is to exploit the power of convolution neural network (CNN) to learn features automatically and thus reduce the dependency on manual feature engineering. We evaluate performance of the proposed model on i2b2-2010 clinical relation extraction challenge dataset. Our results indicate that convolution neural network can be a good model for relation exaction in clinical text without being dependent on expert's knowledge on defining quality features.

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An advanced review on text mining in medicine

Luque

Luna

Luque

et al. 2019

WIREs Data Min & Knowl

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Health care professionals produce abundant textual information in their daily clinical practice and this information is stored in many diverse sources and, generally, in textual form. The extraction of insights from all the gathered information, mainly unstructured and lacking normalization, is one of the major challenges in computational medicine. In this respect, text mining (TM) assembles different techniques to derive valuable insights from unstructured textual data so it has led to be especially relevant in medicine. The aim of this paper is therefore to provide an extensive review of existing techniques and resources to perform TM tasks in medicine. In this review, more than 90 relevant research studies have been analyzed, describing the most important practical applications, terminological resources, tools, and open challenges of TM in medicine. This article is categorized under: Application Areas > Health Care Algorithmic Development > Biological Data Mining Algorithmic Development > Hierarchies and Trees Algorithmic Development > Ensemble Methods

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The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships

Cited by 111 publications

References 9 publications

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions

Relation extraction from clinical texts using domain invariant convolutional neural network

An advanced review on text mining in medicine

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