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A corpus for mining drug-related knowledge from Twitter chatter: Language models and their utilities

Abeed Sarker, Graciela Gonzalez
2017 Data in Brief  
Using this data, which is rich in drug-related chatter, we developed language models to aid the development of data mining tools and methods in this domain.  ...  We collected the data from Twitter using drug names as keywords, including their common misspelled forms.  ...  Extracting adverse drug reaction signals using distributed word representations We tested the possibility of utilizing our distributional semantic language models for exploring associations between drugs  ... 
doi:10.1016/j.dib.2016.11.056 pmid:27981203 pmcid:PMC5144647 fatcat:ylcm5fot3jhplilxh6jcfyj25q

Adverse drug reaction detection via a multihop self-attention mechanism

Tongxuan Zhang, Hongfei Lin, Yuqi Ren, Liang Yang, Bo Xu, Zhihao Yang, Jian Wang, Yijia Zhang
2019 BMC Bioinformatics  
The adverse reactions that are caused by drugs are potentially life-threatening problems. Comprehensive knowledge of adverse drug reactions (ADRs) can reduce their detrimental impacts on patients.  ...  With the growing amount of unstructured textual data, such as biomedical literature and electronic records, detecting ADRs in the available unstructured data has important implications for ADR research  ...  Acknowledgements Authors would like to thank the editor and all anonymous reviewers for valuable suggestions and constructive comments, Authors would also like to thank the Natural Science Foundation of  ... 
doi:10.1186/s12859-019-3053-5 fatcat:amimvc6x7jhbth3dn6h76wqaji

Pharmacovigilance with Transformers: A Framework to Detect Adverse Drug Reactions Using BERT Fine-Tuned with FARM

Sajid Hussain, Hammad Afzal, Ramsha Saeed, Naima Iltaf, Mir Yasir Umair, Murat Sari
2021 Computational and Mathematical Methods in Medicine  
Adverse drug reactions (ADRs) are the undesirable effects associated with the use of a drug due to some pharmacological action of the drug.  ...  This paper presents an end-to-end system for modelling ADR detection from the given text by fine-tuning BERT with a highly modular Framework for Adapting Representation Models (FARM).  ...  [24] used SVM and CRF for extracting adverse drug effects using lexicon-based features, POS tags, word chain, etc.  ... 
doi:10.1155/2021/5589829 pmid:34422092 pmcid:PMC8378963 fatcat:p7ryc7zkfbc2jaxlarohpq62g4

SOCIAL MEDIA MINING SHARED TASK WORKSHOP

ABEED SARKER, AZADEH NIKFARJAM, GRACIELA GONZALEZ
2015 Biocomputing 2016  
We designed three tasks using our in-house annotated Twitter data on adverse drug reactions.  ...  Task 1 involved automatic classification of adverse drug reaction assertive user posts; Task 2 focused on extracting specific adverse drug reaction mentions from user posts; and Task 3, which was slightly  ...  Acknowledgments Our work on social media mining for adverse drug reaction monitoring is supported by the National Institutes of Health (NIH) National Library of Medicine (NLM) grant number NIH  ... 
doi:10.1142/9789814749411_0054 fatcat:55ayzqymird5vgt2mbwm5wztdy

Extended Trigger Terms for Extracting Adverse Drug Reactions in Social Media Texts

Rami Naim Mohammad Yousef, Sabrina Tiun, Nazlia Omar
2019 Journal of Computer Science  
Adverse Drug Reaction (ADR) is a disorder caused by taking medications. Studies have addressed extracting ADRs from social networks where users express their opinion regarding a specific medication.  ...  Furthermore, two document representations have been utilized including Term Frequency Inverse Document Frequency (TFIDF) and Count Vector (CV).  ...  Author's Contributions Rami Naim Mohammad Yousef: selected the topic and analyzed the literature along with proposing and implementing the methods.  ... 
doi:10.3844/jcssp.2019.873.879 fatcat:rko3ealmdrbexagvz3vv35z6t4

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features

A. Nikfarjam, A. Sarker, K. O'Connor, R. Ginn, G. Gonzalez
2015 JAMIA Journal of the American Medical Informatics Association  
Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media.  ...  ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities.  ...  Smith for supervising the annotation process and Pranoti Pimpalkhute, Swetha Jayaraman, and Tejaswi Upadhyaya for their technical support.  ... 
doi:10.1093/jamia/ocu041 pmid:25755127 pmcid:PMC4457113 fatcat:wrekcivgkrf3rbbgl5mdym2c4y

An effective emotional expression and knowledge-enhanced method for detecting adverse drug reactions

Zhengguang Li, Hongfei Lin, Wei Zheng
2020 IEEE Access  
First, the proposed method utilized sentence-level emotional context and word-level emotional score to learn sufficient emotional information for distinguishing between ADR and non-ADR tweets.  ...  Moreover, most of the systems make less use of medical knowledge to enhance the detection of the potential relationship between drugs and adverse reactions in posts.  ...  Then, posts with co-occurrence drug names and adverse reactions are the main objectives in the extraction of adverse reactions.  ... 
doi:10.1109/access.2020.2993169 fatcat:6y7q5ujfdbdh7ief7lfwevtgra

Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets [article]

Shashank Gupta, Manish Gupta, Vasudeva Varma, Sachin Pawar, Nitin Ramrakhiyani, Girish K. Palshikar
2018 arXiv   pre-print
Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care.  ...  Towards this end, we propose a multi-task learning based method which can utilize a similar auxiliary task (adverse drug event detection) to enhance the performance of the main task, i.e., ADR extraction  ...  In this paper, we present two multi-task learning based methods to tackle the problem of labeled data scarcity for adverse drug reaction mention extraction task.  ... 
arXiv:1802.05130v1 fatcat:l4a53cf3sfhtfaesbuirfd3b74

Drug Reaction Discriminator within Encoder-Decoder Neural Network Model: COVID-19 Pandemic Case Study

Hanane Grissette, El Habib Nfaoui
2020 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS)  
Few approaches have proposed in this matter, especially for detecting different drug reaction descriptions from patients generated narratives on social networks.  ...  In this study, we propose to develop an encoder-decoder for drug reaction discrimination that involves an enhanced distributed biomedical representation from controlled medical vocabulary such as PubMed  ...  The evaluation models have slightly different results on Twitter data, where LSTM does well in this case.  ... 
doi:10.1109/snams52053.2020.9336561 fatcat:co7uqgtuunaixnd5tunorkqsd4

Adverse Drug Reaction Detection Using Latent Semantic Analysis

Ahmed Adil Nafea, Nazlia Omar, Mohammed M. AL-Ani
2021 Journal of Computer Science  
Detecting Adverse Drug Reactions (ADRs) is one of the important information for determining the view of the patient on one drug.  ...  Such studies showed remarkable performance in terms of extracting ADR.  ...  The authors would also like to thank the UKM for funding the research.  ... 
doi:10.3844/jcssp.2021.960.970 fatcat:tecvopfgdng5zj6gzb6v22e7r4

A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance.  ...  Unlike the other existing techniques, our approach is capable to extracting fine-grained information (such as 'Indications', 'Symptoms', 'Finding', 'Disease', 'Drug') which provide important cues in pharmacovigilance  ...  use very implicit and creative language to describe their adverse drug reaction.  ... 
doi:10.18653/v1/p19-1516 dblp:conf/acl/YadavESB19 fatcat:hteuwz2sh5hkzdvanj7bjcy5u4

Adverse Drug Reaction Detection in Social Media by Deepm Learning Methods

Zahra Rezaei, Hossein Ebrahimpour-Komleh, Behnaz Eslami, Ramyar Chavoshinejad, Mehdi Totonchi
2019 Cell journal  
Social media, such as Twitter, has become a valuable online tool to describe the early detection of various adverse drug reactions (ADRs).  ...  This study aimed to demonstrate a quick and accurate method to collect and classify information based on the distribution of approved data on Twitter.  ...  Acknowledgements The authors have no proprietary, financial, professional, or other personal interest of any nature in any product, service, or company. There is no conflict of interest in this study.  ... 
doi:10.22074/cellj.2020.6615 pmid:31863657 pmcid:PMC6947008 fatcat:kizwy4mi7nbflfs37qq56zmouu

Feature Engineering for Recognizing Adverse Drug Reactions from Twitter Posts

Hong-Jie Dai, Musa Touray, Jitendra Jonnagaddala, Shabbir Syed-Abdul
2016 Information  
With more people discussing their health information online publicly, social media platforms present a rich source of information for exploring adverse drug reactions (ADRs).  ...  for the entities in the ADR-R task are different from entities in general 486 domains.  ...  IOB: B-ADR, I-ADR. ‚ Word representation feature: The large unlabeled data from the Twitter website was utilized to generate word clusters for all of the unique tokens with the vector representation method  ... 
doi:10.3390/info7020027 fatcat:tepk5yq5uvdnlhycgf2vskn2s4

Enhancing Pharmacovigilance with Drug Reviews and Social Media [article]

Brent Biseda, Katie Mo
2020 arXiv   pre-print
This paper explores whether the use of drug reviews and social media could be leveraged as potential alternative sources for pharmacovigilance of adverse drug reactions (ADRs).  ...  The tasks include sentiment classification of drug reviews, presence of ADR in twitter postings, and named entity recognition of ADRs in twitter postings.  ...  Conditional random fields (CRFs) was used in conjunction with K-means clustering on word2vec embeddings in a previous study for named entity recognition of adverse drug reactions and resulted in an F-score  ... 
arXiv:2004.08731v1 fatcat:m33xnnmjgzd5vijdqcjdebf6bm

Combination of Deep Recurrent Neural Networks and Conditional Random Fields for Extracting Adverse Drug Reactions from User Reviews

Elena Tutubalina, Sergey Nikolenko
2017 Journal of Healthcare Engineering  
Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions.  ...  We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.  ...  Qualitative Analysis of Extracted ADR Mentions. Adverse drug reactions can differ significantly depending on the patient.  ... 
doi:10.1155/2017/9451342 pmid:29177027 pmcid:PMC5605929 fatcat:jmjn4o3mtneilbuc37dt2gf5uq
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