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Adverse event detection by integrating twitter data and VAERS
2018
Journal of Biomedical Semantics
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data and the Vaccine Adverse Event Reporting System (VAERS) information aiming to identify potential AEs after influenza vaccine. ...
Results: To tackle both challenges from traditional reporting systems and social media, we exploit their complementary strength and develop a combinatorial classification approach by integrating Twitter ...
VAERS: Vaccine Adverse Event Reporting System. ...
doi:10.1186/s13326-018-0184-y
pmid:29925405
pmcid:PMC6011255
fatcat:fz4t7rv6kzdopo3x4fmageid5y
Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data
2022
Vaccines
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The goal of this project is to develop a machine learning and natural language processing approach to identify COVID-19 vaccine adverse events (VAE) from Twitter data. ...
To the best of our knowledge, this is the first study to identify COVID-19 vaccine adverse event signals from social media. ...
We also thank Meng Zhang and Jingna Feng for the Twitter data annotation.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/vaccines10010103
pmid:35062764
pmcid:PMC8781534
fatcat:fwszstfmercwhkyb43itslahga
Multi-instance Domain Adaptation for Vaccine Adverse Event Detection
[article]
2020
arXiv
pre-print
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Detection of vaccine adverse events is crucial to the discovery and improvement of problematic vaccines. ...
Case studies showed that formal reports and extracted adverse-relevant tweets by MIDA shared a similarity of keyword and description patterns. ...
Dataset Description The task of the first dataset is to detect whether Twitter users are affected by adverse event according to their tweets. The dataset consists of Twitter data and formal reports. ...
arXiv:2009.04901v1
fatcat:zlerjhizzbboxg3jdigdte3bzq
Multi-instance Domain Adaptation for Vaccine Adverse Event Detection
2018
Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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Detection of vaccine adverse events is crucial to the discovery and improvement of problematic vaccines. ...
Case studies showed that formal reports and extracted adverse-relevant tweets by MIDA shared a similarity of keyword and description patterns. ...
Dataset Description The task of the first dataset is to detect whether Twitter users are affected by adverse event according to their tweets. The dataset consists of Twitter data and formal reports. ...
doi:10.1145/3178876.3186051
dblp:conf/www/Wang018
fatcat:w4mgqwhdenhdnoi74vv7fndt4e
Vaccine Safety Resources for Nurses
2015
The American Journal of Nursing
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VSD data can be used to detect safety problems and to assess and quantify the risk of adverse events. 15 A VSD study was able to quantify the risk of anaphylaxis following vaccination in general in children ...
Datalink (VSD) uses electronic health record data from nine integrated health care organizations to conduct surveillance and epidemiologic studies. ...
doi:10.1097/01.naj.0000470404.74424.ee
pmid:26222474
pmcid:PMC4599699
fatcat:j2757fs5mzhvjfimkd4s7kdfo4
Vaccine Safety
[chapter]
2018
Plotkin's Vaccines
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Acknowledgments We are grateful to Robert Davis, Deborah Gust, Robert Chen, and Charles Hackett who contributed sections of this chapter in previous editions of this book. ...
Initial safety data were provided by VAERS, which found that the adverse event profile after 2009-H1N1 vaccine in VAERS (>10,000 reports) was consistent with that of seasonal influenza vaccines. 91, 138 ...
[83] [84] [85] Some increases in adverse events detected by VAERS might not be true increases, but instead might be the result of increases in reporting efficiency or vaccine coverage. ...
doi:10.1016/b978-0-323-35761-6.00082-1
fatcat:opzmvotoqfgyjjy5bmgni2opju
Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches
2019
Frontiers in Pharmacology
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The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. ...
Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific ...
The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. ...
doi:10.3389/fphar.2019.00415
pmid:31156424
pmcid:PMC6533857
fatcat:pljcyqcp6red7pdwa5burxgbx4
Big Data and Pharmacovigilance: Data Mining for Adverse Drug Events and Interactions
2018
P & T : a peer-reviewed journal for formulary management
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A "big data" approach to pharmacovigilance involves the identification of drug-ADE associations by data mining various electronic sources, including: adverse event reports, the medical literature, electronic ...
Adverse drug events (ADEs), including drug interactions, have a tremendous impact on patient health and generate substantial health care costs. ...
Event Text Mining (VaeTM) system to detect drug-ADE associations present in the text in the SRS database that it maintains for vaccines, the Vaccine Adverse Event Reporting System (VAERS). 2 This software ...
pmid:29896033
pmcid:PMC5969211
fatcat:rpjrb2p2rngdtd4ap6yaqr2rtq
Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety
[chapter]
2018
Pharmacovigilance [Working Title]
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Post-marketing data sources Not only notification of suspected adverse drug events is mandatory for health professionals, but also other subjects can report events to the relevant regulatory authorities ...
After a panorama on key data sources and analyses of post-marketing data of adverse drug reactions, a critical appraisal of methodological issues and debated future applications of SRSs will be presented ...
/cdrh/cfdocs/cfMAUDE/search.CFM), VAERS-Vaccines Adverse Event Reporting System (https://vaers.hhs.gov/data/datasets.html), and CAERS-Center for Food Safety and Applied Nutrition Adverse Event Reporting ...
doi:10.5772/intechopen.79986
fatcat:bguoxablmzbqxhqilyfxeltbwi
Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is ("Isolate–Inactivate–Inject") Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview
2018
Frontiers in Public Health
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This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. ...
role to a science, characterized by a rational design and plan ("vaccinology 3.0"). ...
side effects
Vaccine adverse event reporting system (VAERS)
Vaccine adverse event ontology
Adversomics
Digital epidemiology/infodemiology and infoveillance
Vaccine literacy/vaccine
hesitancy ...
doi:10.3389/fpubh.2018.00062
pmid:29556492
pmcid:PMC5845111
fatcat:ctinubftg5gdnniwdmzj26gura
Detecting Epidemic Diseases Using Sentiment Analysis of Arabic Tweets
2020
Journal of universal computer science (Online)
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In this study, a new approach is proposed in order to detect Influenza using machine learning techniques from Arabic tweets in Arab countries. ...
Opinion mining is an important step towards facilitating information in health data. Several studies have demonstrated the possibility of tracking diseases using public tweets. ...
Acknowledgment Thanks to Jordan University of Science and Technology for supporting this publication under Award Number 20170030. ...
doi:10.3897/jucs.2020.004
fatcat:kcfhy26parc2pgykpwm33jw4ba
International Society for Disease Surveillance Conference 2010
2011
Emerging Health Threats Jour
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Using Twitter to estimate H1N1 influenza activity A Signorini, PM Polgreen, and AM Segre 84. ...
A comparison of syndromic surveillance chief complaint data and discharge data in a pediatric hospital system during 2009 H1N1 D Peace, W Smith, A Reeves, D Little, K Soetebier, and C Drenzek 89. ...
Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. ...
doi:10.3402/ehtj.v4i0.7174
pmid:24149039
pmcid:PMC3168218
fatcat:r34ofgptjbhbblmg7k2xwtcxkq
An ensemble of neural models for nested adverse drug events and medication extraction with subwords
2019
JAMIA Journal of the American Medical Informatics Association
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To better represent rare and unknown words in entities, we further tokenized the MIMIC III data set by splitting the words into finer-grained subwords. ...
This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2. ...
ACKNOWLEDGMENTS The authors thank Artificial Intelligence Research Centre (AIRC), National Institute of Advanced Industrial Science and Technology (AIST) for the computational resources. ...
doi:10.1093/jamia/ocz075
pmid:31197355
pmcid:PMC6913208
fatcat:dl3zf6p5vjhsff7trbclyh2bfq
Prevention and control of HPV infection and HPV-related cancers in Colombia- a meeting report
2020
BMC Proceedings
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In response to drastic drop of vaccine coverage following the adverse event crisis in Carmen del Bolivar, Colombia, the HPV Prevention and Control Board in collaboration with the Colombian National Cancer ...
The conclusion of the meeting included following suggestions to strengthen HPV prevention and control: 1) Re-introducing school-based vaccine programs, 2) Integrating primary and secondary prevention programs ...
Acknowledgements We thank the session chairs and speakers for their valuable slides, presentations, and comments; and the meeting participants for their thorough and insightful discussions. ...
doi:10.1186/s12919-020-00192-2
pmid:32577128
pmcid:PMC7307134
fatcat:ioldqqc7zvg53cbrrasctcguzm
Identifying Cases of Shoulder Injury Related to Vaccine Administration (SIRVA) Using Natural Language Processing
[article]
2021
medRxiv
pre-print
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The NLP algorithm can potentially be used in future population-based studies to identify this rare adverse event, avoiding labor-intensive chart review validation. ...
Methods: We conducted the study among members of a large integrated health care organization who were vaccinated between 04/1/2016 and 12/31/2017 and had subsequent diagnosis codes indicative of shoulder ...
, including the clinical notes of the study subjects, Vaccine Adverse Event Reporting System (VAERS) reports [31] , ontologies (e.g., Unified Medical Language System (UMLS) [32] ), semantic lexicons ...
doi:10.1101/2021.05.05.21256555
fatcat:kepivcrkkfdyhjlpxlor3vtpxm
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