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Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels

Mert Tiftikci, Arzucan Özgür, Yongqun He, Junguk Hur
<span title="2019-12-23">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
Our study demonstrates that a system composed of a deep learning architecture for entity recognition and a rule-based model for entity normalization is a promising approach for ADR extraction from drug  ...  In this paper, we present a machine learning- and rule-based system for the identification of ADR entity mentions in the text of drug labels and their normalization through the Medical Dictionary for Regulatory  ...  Disclaimer Part of the content described in this paper was presented at the TAC 2017 Workshop and published online as a non-peer reviewed conference proceedings paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-019-3195-5">doi:10.1186/s12859-019-3195-5</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31865904">pmid:31865904</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6927101/">pmcid:PMC6927101</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pd253fjy3rbkzdyyulb24uyhsm">fatcat:pd253fjy3rbkzdyyulb24uyhsm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191224004742/https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-019-3195-5" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/74/bb/74bbc68c3821d3c2e980d4d15dddd8b71d52c862.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-019-3195-5"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927101" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records

Feifan Liu, Abhyuday Jagannatha, Hong Yu
<span title="">2019</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gwjfiddrarc4zhvrdexu2pibom" style="color: black;">Drug Safety</a> </i> &nbsp;
Part of a theme issue on "NLP Challenge for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0)" guest edited by Feifan Liu, Abhyuday Jagannatha and Hong Yu.  ...  We also thank all the reviewers for their comments and thoughtful suggestions for improving the submitted drafts.  ...  Second, the authors introduce the three subtasks defined in the challenge: Named Entity Recognition (NER), Relation Identification (RI), and Joint Relation Extraction (NER-RI), followed by a comprehensive  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s40264-018-0766-8">doi:10.1007/s40264-018-0766-8</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30649734">pmid:30649734</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6842570/">pmcid:PMC6842570</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pwdrt74tpfhvrozqvzg3bslmyy">fatcat:pwdrt74tpfhvrozqvzg3bslmyy</a> </span>
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Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

G. Gonzalez-Hernandez, A. Sarker, K. O'Connor, G. Savova
<span title="">2017</span> <i title="Schattauer GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/25ybzim6mvglde3zgjvka7ehii" style="color: black;">IMIA Yearbook of Medical Informatics</a> </i> &nbsp;
Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in  ...  Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15265/iy-2017-029">doi:10.15265/iy-2017-029</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29063568">pmid:29063568</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6250990/">pmcid:PMC6250990</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i6kvjcddgnbfvojmloral5pncu">fatcat:i6kvjcddgnbfvojmloral5pncu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180724052542/https://www.thieme-connect.de/products/ejournals/pdf/10.15265/IY-2017-029.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/20/25/2025e4807d45b409a2d76a430a754f790f5a396c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15265/iy-2017-029"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6250990" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Event Extraction Based on Deep Learning in Food Hazard Arabic Texts [article]

Fouzi Harrag, Selmene Gueliani
<span title="2020-08-11">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We proposed here a model based on deep recurrent networks to extract the events from social media feeds.  ...  Exchanging textual data is the most popular communication among social media users. It has become a necessity for treatment.  ...  Data mining techniques for event detection from social media Social media and internet-based data are becoming a main source for the agencies and people who are looking for information about health and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.05014v1">arXiv:2008.05014v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ghtuhrp2cvdnlcl3bukyihea44">fatcat:ghtuhrp2cvdnlcl3bukyihea44</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826040138/https://arxiv.org/ftp/arxiv/papers/2008/2008.05014.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/23/62/2362dd0e058de601693587dc3f69ee60b36c5e6a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.05014v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

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

G. Gonzalez-Hernandez, A. Sarker, K. O'Connor, G. Savova
<span title="">2017</span> <i title="Georg Thieme Verlag KG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/25ybzim6mvglde3zgjvka7ehii" style="color: black;">IMIA Yearbook of Medical Informatics</a> </i> &nbsp;
Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in  ...  Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1055/s-0037-1606506">doi:10.1055/s-0037-1606506</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ujcstkryrzhgpalibaaduzchfy">fatcat:ujcstkryrzhgpalibaaduzchfy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180724052542/https://www.thieme-connect.de/products/ejournals/pdf/10.15265/IY-2017-029.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/20/25/2025e4807d45b409a2d76a430a754f790f5a396c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1055/s-0037-1606506"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

ASPECTS OF UTILIZATION AND LIMITATIONS OF ARTIFICIAL INTELLIGENCE IN DRUG SAFETY

SUJITH T, CHAKRADHAR T, SRAVANI MARPAKA, SOWMINI K
<span title="2021-08-07">2021</span> <i title="Innovare Academic Sciences Pvt Ltd"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/exe2cs2mnnd5xor5poxecc5qlu" style="color: black;">Asian Journal of Pharmaceutical and Clinical Research</a> </i> &nbsp;
profile of drugs and to take timely action for the well-being of people.  ...  Introducing AI will potentially fulfill the limitations in these areas and help us to use the resources in a focused way to get the real-world risk-benefit ratio for a better understanding of the safety  ...  ACKNOWLEDGMENT We acknowledge Osmania Medical College and National Coordination Centre -PV Programme of India, Indian Pharmacopoeia Commission, Ministry of health and family welfare for technical support  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22159/ajpcr.2021.v14i8.41979">doi:10.22159/ajpcr.2021.v14i8.41979</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5femlqpv3jef7lasqbilgbp2nm">fatcat:5femlqpv3jef7lasqbilgbp2nm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210905021133/https://innovareacademics.in/journals/index.php/ajpcr/article/download/41979/25224" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/24/42/2442cf071125c38d126723f2243dc8de18615cf5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22159/ajpcr.2021.v14i8.41979"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

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

Elena Tutubalina, Sergey Nikolenko
<span title="">2017</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cswd2rqrire6lgrsm56kv4adue" style="color: black;">Journal of Healthcare Engineering</a> </i> &nbsp;
Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions.  ...  In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields.  ...  See [39] for a comprehensive review of ADR extraction from social media data with NLP-based approaches. Supervised machine learning techniques have been successfully applied to detect ADRs.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2017/9451342">doi:10.1155/2017/9451342</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29177027">pmid:29177027</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5605929/">pmcid:PMC5605929</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jmjn4o3mtneilbuc37dt2gf5uq">fatcat:jmjn4o3mtneilbuc37dt2gf5uq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190307031351/http://pdfs.semanticscholar.org/f236/5ab0fdd00ad2c99758b59a2f2ccae702dc7b.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f2/36/f2365ab0fdd00ad2c99758b59a2f2ccae702dc7b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2017/9451342"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5605929" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries [article]

Maksim Belousov, Nikola Milosevic, Ghada Alfattni, Haifa Alrdahi, Goran Nenadic
<span title="2019-09-23">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, and adverse events) from hospital discharge  ...  summaries through deep learning that relies on various representations for clinical named entity recognition.  ...  This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.10390v1">arXiv:1909.10390v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nd7th73xlfap7latjetswrc5mm">fatcat:nd7th73xlfap7latjetswrc5mm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200913063609/https://arxiv.org/ftp/arxiv/papers/1909/1909.10390.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3d/7a/3d7ade5853639e4e1c244ab40c854d7b60b32d99.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.10390v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning

Tsendsuren Munkhdalai, Feifan Liu, Hong Yu
<span title="2018-04-25">2018</span> <i title="JMIR Publications Inc."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/l4d2cstvknamhpnofbe3dxtlpe" style="color: black;">JMIR Public Health and Surveillance</a> </i> &nbsp;
Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance.  ...  This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and relations in the context of drug safety surveillance, and investigate  ...  Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect those of the sponsor. Conflicts of Interest None declared.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2196/publichealth.9361">doi:10.2196/publichealth.9361</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29695376">pmid:29695376</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5943628/">pmcid:PMC5943628</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ehfpn3dq35hvlbver4uzzr4h3e">fatcat:ehfpn3dq35hvlbver4uzzr4h3e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190426134239/https://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=4450&amp;context=oapubs" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/be/f4/bef46175615cc45b6f2c67f31dd087f304129280.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2196/publichealth.9361"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5943628" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Pharmacovigilance through the development of text mining and natural language processing techniques

Isabel Segura-Bedmar, Paloma Martínez
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p4kk6lusgrhyxecgig72iasi5q" style="color: black;">Journal of Biomedical Informatics</a> </i> &nbsp;
Ted Shortliffe for giving us the opportunity to organize this special issue and for his great and endless help and patience throughout the process; and the JBI staff for the ongoing support.  ...  comments; the JBI Editor-in-Chief, Dr.  ...  [19] present a machine-learning system that covers all tasks required to extract and classify drug-drug interactions automatically: drug name recognition, DDI extraction and DDI classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jbi.2015.11.001">doi:10.1016/j.jbi.2015.11.001</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26547007">pmid:26547007</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v5ev5ulpbfhm7k4u46mphz2y3e">fatcat:v5ev5ulpbfhm7k4u46mphz2y3e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171002121152/http://publisher-connector.core.ac.uk/resourcesync/data/elsevier/pdf/b6e/aHR0cDovL2FwaS5lbHNldmllci5jb20vY29udGVudC9hcnRpY2xlL3BpaS9zMTUzMjA0NjQxNTAwMjM4NQ%3D%3D.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/02/e4/02e48d3ca02ea8743974b9faa58bfa7b1b0c906d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jbi.2015.11.001"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
<span title="2016-06-26">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.  ...  Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological  ...  The tasks include named entity recognition [9] [10] [11] , event identification [12] [13] [14] , and relation extraction [10, 15, 16] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.07993v1">arXiv:1606.07993v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7d5om7zxxzhoviiriasrfwg3xi">fatcat:7d5om7zxxzhoviiriasrfwg3xi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191024193732/https://arxiv.org/pdf/1606.07993v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e8/f7/e8f7e63ef52e6c19a0933cf7d920fcc2ad60eab7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.07993v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

AI-based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox Scandal in 2017 on Doctissimo Forum [article]

Valentin Roche, Jean-Philippe Robert, Hanan Salam
<span title="2022-02-01">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Various approaches have investigated the analysis of social media data using AI such as NLP techniques for detecting adverse drug events.  ...  Existing approaches have focused on the extraction and identification of Adverse Drug Reactions, Drug-Drug Interactions and drug misuse.  ...  Deep Learning Approaches Recently, deep learning has attracted researchers to propose approaches for ADR detection and extraction from social media [18] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.03538v1">arXiv:2203.03538v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/guji5cqfy5b6jb2m4k4tv3ixtu">fatcat:guji5cqfy5b6jb2m4k4tv3ixtu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220311225159/https://arxiv.org/pdf/2203.03538v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/74/22/7422bc258b1156a05cb48524d5009ef091992041.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.03538v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Utilizing social media data for pharmacovigilance: A review

Abeed Sarker, Rachel Ginn, Azadeh Nikfarjam, Karen O'Connor, Karen Smith, Swetha Jayaraman, Tejaswi Upadhaya, Graciela Gonzalez
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p4kk6lusgrhyxecgig72iasi5q" style="color: black;">Journal of Biomedical Informatics</a> </i> &nbsp;
In this paper, we perform a methodical review to characterize the different approaches to ADR detection/extraction from social media, and their applicability to pharmacovigilance.  ...  In recent years, user-posted data on social media, primarily due to its sheer volume, has become a useful resource for ADR monitoring.  ...  Acknowledgement We thank the NIH/NLM support for this project (Award: 1R01LM011176-01).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jbi.2015.02.004">doi:10.1016/j.jbi.2015.02.004</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25720841">pmid:25720841</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4408239/">pmcid:PMC4408239</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wgtsyh7unbgupilvlbaen4gjbe">fatcat:wgtsyh7unbgupilvlbaen4gjbe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171006015809/http://publisher-connector.core.ac.uk/resourcesync/data/elsevier/pdf/ff8/aHR0cDovL2FwaS5lbHNldmllci5jb20vY29udGVudC9hcnRpY2xlL3BpaS9zMTUzMjA0NjQxNTAwMDM2Mg%3D%3D.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/37/38/3738e35f7b288f8689c41bbe9d5a6a6b0c430831.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jbi.2015.02.004"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408239" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

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

Hanane Grissette, El Habib Nfaoui
<span title="2020-12-14">2020</span> <i title="IEEE"> 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS) </i> &nbsp;
Social networks become widely used for understanding patients shared experiences, and reaching a vast audience in a matter of seconds.  ...  Few approaches have proposed in this matter, especially for detecting different drug reaction descriptions from patients generated narratives on social networks.  ...  approach to achieve a variety of features, including a novel feature for modeling words' semantic similarities from a highly informal text in social media.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/snams52053.2020.9336561">doi:10.1109/snams52053.2020.9336561</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/co7uqgtuunaixnd5tunorkqsd4">fatcat:co7uqgtuunaixnd5tunorkqsd4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210427073514/https://ieeexplore.ieee.org/ielx7/9336519/9336528/09336561.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/cf/d3cff693390dd5b740ddbf0d0c8ce55ddfc5bb84.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/snams52053.2020.9336561"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Survey of Text-based Epidemic Intelligence

Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cécile Paris, C. Raina Macintyre
<span title="2019-10-16">2019</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eiea26iqqjcatatlgxdpzt637y" style="color: black;">ACM Computing Surveys</a> </i> &nbsp;
We view past work in terms of two broad categories: health mention classification (selecting relevant text from a large volume) and health event detection (predicting epidemic events from a collection  ...  In this survey, we discuss approaches for epidemic intelligence that use textual datasets, referring to it as "text-based epidemic intelligence."  ...  In the concept extraction step, they use taggers for named entity recognition and information extraction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3361141">doi:10.1145/3361141</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qcfn7oqwvrfvtjumpeocdtjnfm">fatcat:qcfn7oqwvrfvtjumpeocdtjnfm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200515160054/https://dl.acm.org/doi/pdf/10.1145/3361141?download=true" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2d/51/2d51acd897f021a0a5c041ee678ba3b75a018f66.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3361141"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>
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