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Towards large-scale twitter mining for drug-related adverse events

Authors:

Umit Topaloglu,

Fan YuAuthors Info & Claims

SHB '12: Proceedings of the 2012 international workshop on Smart health and wellbeing

Pages 25 - 32

https://doi.org/10.1145/2389707.2389713

Published: 29 October 2012 Publication History

Abstract

Drug-related adverse events pose substantial risks to patients who consume post-market or Drug-related adverse events pose substantial risks to patients who consume post-market or investigational drugs. Early detection of adverse events benefits not only the drug regulators, but also the manufacturers for pharmacovigilance. Existing methods rely on patients' "spontaneous" self-reports that attest problems. The increasing popularity of social media platforms like the Twitter presents us a new information source for finding potential adverse events. Given the high frequency of user updates, mining Twitter messages can lead us to real-time pharmacovigilance. In this paper, we describe an approach to find drug users and potential adverse events by analyzing the content of twitter messages utilizing Natural Language Processing (NLP) and to build Support Vector Machine (SVM) classifiers. Due to the size nature of the dataset (i.e., 2 billion Tweets), the experiments were conducted on a High Performance Computing (HPC) platform using MapReduce, which exhibits the trend of big data analytics. The results suggest that daily-life social networking data could help early detection of important patient safety issues.

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Cited By

Peng QRen Y(2024)A Hybrid Neural Network Model with Entity-related Knowledge for Adverse Drug Reaction Detection2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822078(7117-7120)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822078
Wessel DPogrebnyakov N(2024)Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision MakingDrug Safety10.1007/s40264-024-01409-547:5(495-511)Online publication date: 6-Mar-2024
https://doi.org/10.1007/s40264-024-01409-5
Gao YXu AHu P(2024)Mining Patient-Generated Content for Medication Relations and Transition Network to Predict the Rankings and Volumes of Different MedicationsInformation Systems Frontiers10.1007/s10796-024-10530-wOnline publication date: 7-Sep-2024
https://doi.org/10.1007/s10796-024-10530-w
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Index Terms

Towards large-scale twitter mining for drug-related adverse events
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

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Published In

cover image ACM Conferences

SHB '12: Proceedings of the 2012 international workshop on Smart health and wellbeing

October 2012

72 pages

ISBN:9781450317122

DOI:10.1145/2389707

Program Chairs:
Christopher C. Yang
Drexel University, USA
,
Hsinchun Chen
University of Arizona, USA
,
Howard Wactlar
National Science Foundation, USA
,
Carlo Combi
Universita' degli Studi di Verona, Italy
,
Xuning Tang
Drexel University, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 29 October 2012

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29, 2012

Hawaii, Maui, USA

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Cited By

Peng QRen Y(2024)A Hybrid Neural Network Model with Entity-related Knowledge for Adverse Drug Reaction Detection2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822078(7117-7120)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822078
Wessel DPogrebnyakov N(2024)Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision MakingDrug Safety10.1007/s40264-024-01409-547:5(495-511)Online publication date: 6-Mar-2024
https://doi.org/10.1007/s40264-024-01409-5
Gao YXu AHu P(2024)Mining Patient-Generated Content for Medication Relations and Transition Network to Predict the Rankings and Volumes of Different MedicationsInformation Systems Frontiers10.1007/s10796-024-10530-wOnline publication date: 7-Sep-2024
https://doi.org/10.1007/s10796-024-10530-w
Ainapure BPise RReddy PAppasani BSrinivasulu AKhan MBizon N(2023)Sentiment Analysis of COVID-19 Tweets Using Deep Learning and Lexicon-Based ApproachesSustainability10.3390/su1503257315:3(2573)Online publication date: 31-Jan-2023
https://doi.org/10.3390/su15032573
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Oyebode OOrji R(2023)Identifying adverse drug reactions from patient reviews on social media using natural language processingHealth Informatics Journal10.1177/1460458222113671229:1(146045822211367)Online publication date: 1-Mar-2023
https://doi.org/10.1177/14604582221136712
Nishioka SAsano MYada SAramaki EYajima HYanagisawa YSayama KKizaki HHori S(2023)Adverse event signal extraction from cancer patients’ narratives focusing on impact on their daily-life activitiesScientific Reports10.1038/s41598-023-42496-113:1Online publication date: 19-Sep-2023
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Unnikrishnan RS. SV.S. A(2023)Efficient parameter tuning of neural foundation models for drug perspective prediction from unstructured socio-medical dataEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106214123:PAOnline publication date: 1-Aug-2023
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Döring NDöring N(2023)Computationale Methoden in den Sozial- und HumanwissenschaftenForschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften10.1007/978-3-662-64762-2_19(1011-1062)Online publication date: 23-Feb-2023
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Qiu YZhang XWang WZhang TXu BLin H(2023)KESDT: Knowledge Enhanced Shallow and Deep Transformer for Detecting Adverse Drug ReactionsNatural Language Processing and Chinese Computing10.1007/978-3-031-44696-2_47(601-613)Online publication date: 8-Oct-2023
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