Cadec: A corpus of adverse drug event annotations

Karimi, Sarvnaz; Metke-Jimenez, Alejandro; Kemp, Madonna; Wang, Chen

doi:10.1016/j.jbi.2015.03.010

Cited by 201 publications

(151 citation statements)

References 21 publications

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“…We perform an extensive evaluation of neural models on three datasets of UGTs, namely CADEC (Karimi et al, 2015), Psy-TAR (Zolnoori et al, 2019), and SMM4H 2017 (Sarker et al, 2018). The basic task is to map a social media phrase to a relevant medical concept.…”

Section: Methodsmentioning

confidence: 99%

Deep Neural Models for Medical Concept Normalization in User-Generated Texts

Miftahutdinov¹,

Tutubalina²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS). This is a challenging task since medical terminology is very different when coming from health care professionals or from the general public in the form of social media texts. We approach it as a sequence learning problem with powerful neural networks such as recurrent neural networks and contextualized word representation models trained to obtain semantic representations of social media expressions. Our experimental evaluation over three different benchmarks shows that neural architectures leverage the semantic meaning of the entity mention and significantly outperform an existing state of the art models.

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Section: Methodsmentioning

confidence: 99%

Deep Neural Models for Medical Concept Normalization in User-Generated Texts

Miftahutdinov¹,

Tutubalina²

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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“…In domains close to PGx, corpora have been annotated with biomedical entities, but only few of them include relationships (see Hahn et al [14] for a panorama, plus [20,26,42,51]). Closer corpora are related to pharmacovigilance or genetic traits, then focusing on drug-adverse response or SNP-phenotype associations.…”

Section: Description Of the Dataset: Pgxcorpusmentioning

confidence: 99%

PGxCorpus: a Manually Annotated Corpus for Pharmacogenomics

Legrand

Gogdemir

Bousquet

et al. 2019

Preprint

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Pharmacogenomics (PGx) studies how individual gene variations impact drug response phenotypes, which makes knowledge related to PGx a key component towards precision medicine. A significant part of the state-of-the-art knowledge in PGx is accumulated in scientific publications, where it is hardly reusable to humans or software. Natural language processing techniques have been developed and employed for guiding experts curating this amount of knowledge. But existing works are limited by the absence of a high quality annotated corpus focusing on PGx domain. This absence restricts in particular the use of supervised machine learning. This article introduces PGxCorpus, a manually annotated corpus, designed to fill this gap and to enable the automatic extraction of PGx relationships from text. It comprises 945 sentences from 911 PubMed abstracts, annotated with PGx entities of interest (mainly gene variations, genes, drugs and phenotypes), and relationships between those. We present in this article the corpus itself, its construction and a baseline experiment that illustrates how it may be leveraged to synthesize and summarize PGx knowledge.

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“…In social media, discontinuous ADR mentions also usually appear. Karimi et al [37] annotated a corpus of adverse drug events including both continuous and discontinuous ADR mentions, that is, CADEC. To recognize continuous and discontinuous ADR mentions simultaneously, MetkeJimenez and Karimi [38] followed Tang et al 's [36] way to represent them in a unified schema and used CRFs with baseline features, including bag-of-words, character n-grams, and word shapes.…”

Section: Related Workmentioning

confidence: 99%

Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM‐CRF

Tang

Wang

et al. 2018

Wireless Communications and Mobile Computing

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Social media in medicine, where patients can express their personal treatment experiences by personal computers and mobile devices, usually contains plenty of useful medical information, such as adverse drug reactions (ADRs); mining this useful medical information from social media has attracted more and more attention from researchers. In this study, we propose a deep neural network (called LSTM-CRF) combining long short-term memory (LSTM) neural networks (a type of recurrent neural networks) and conditional random fields (CRFs) to recognize ADR mentions from social media in medicine and investigate the effects of three factors on ADR mention recognition. The three factors are as follows: (1) representation for continuous and discontinuous ADR mentions: two novel representations, that is, "BIOHD" and "Multilabel," are compared; (2) subject of posts: each post has a subject (i.e., drug here); and (3) external knowledge bases. Experiments conducted on a benchmark corpus, that is, CADEC, show that LSTM-CRF achieves better -score than CRF; "Multilabel" is better in representing continuous and discontinuous ADR mentions than "BIOHD"; both subjects of comments and external knowledge bases are individually beneficial to ADR mention recognition. To the best of our knowledge, this is the first time to investigate deep neural networks to mine continuous and discontinuous ADRs from social media.

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Cadec: A corpus of adverse drug event annotations

Cited by 201 publications

References 21 publications

Deep Neural Models for Medical Concept Normalization in User-Generated Texts

Deep Neural Models for Medical Concept Normalization in User-Generated Texts

PGxCorpus: a Manually Annotated Corpus for Pharmacogenomics

Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM‐CRF

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