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Utilizing Deep Learning to Identify Drug Use on Twitter Data [article]

Joseph Tassone, Peizhi Yan, Mackenzie Simpson, Chetan Mendhe, Vijay Mago, Salimur Choudhury
2020 arXiv   pre-print
Through the analysis of collected Twitter data, models were developed for classifying drug-related tweets.  ...  Rather than simple feature or attribute analysis, a deep learning approach was implemented to screen and analyze the tweets' semantic meaning.  ...  Lastly, they would like to acknowledge Caleb Pears for acting as a substance and addictions consultant, and verifying the keyword selection.  ... 
arXiv:2003.11522v1 fatcat:kxr7pbvvlvdkjhhjhg3zf7prxy

Utilizing deep learning and graph mining to identify drug use on Twitter data

Joseph Tassone, Peizhi Yan, Mackenzie Simpson, Chetan Mendhe, Vijay Mago, Salimur Choudhury
2020 BMC Medical Informatics and Decision Making  
For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets.  ...  Methods Social media data (tweets and attributes) were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption).  ...  Lastly, they would like to acknowledge Caleb Pears for acting as a substance and addictions consultant, and verifying the keyword selection.  ... 
doi:10.1186/s12911-020-01335-3 pmid:33380324 fatcat:nqnt3q2glvgwhlmxvzletp2bga

Mining's Twitter Stream Grab for Pharmacovigilance Research Gold [article]

Ramya Tekumalla, Javad Rafiei Asl, Juan M. Banda
2019 biorxiv/medrxiv   pre-print
Knowing that not everything that shines is gold, we used pre-existing drug-related datasets to build machine learning models to filter our findings for relevance.  ...  In this work we present our methodology and the 3,346,758 identified tweets for public use in future research.  ...  In such cases, we can share the data on request while adhering to the Twitter data sharing policy.  ... 
doi:10.1101/859611 fatcat:i6kid4zyo5fcpi4rd2uawz2ofm

A scoping review of the use of Twitter for public health research

Oduwa Edo-Osagie, Beatriz De La Iglesia, Iain Lake, Obaghe Edeghere
2020 Computers in Biology and Medicine  
Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society.  ...  From our review, we were able to obtain a clear picture of the use of Twitter for public health.  ...  While it was rare, one study made use of semi-supervised learning and deep learning to simulate influenza epidemics.  ... 
doi:10.1016/j.compbiomed.2020.103770 pmid:32425212 pmcid:PMC7229729 fatcat:sxddp57merbbrm5ulftfu2ufsa

A large-scale Twitter dataset for drug safety applications mined from publicly existing resources [article]

Ramya Tekumalla, Juan M. Banda
2020 arXiv   pre-print
data keeps flowing in Twitter.  ...  We provide all code and detailed procedure on how to extract this dataset and the selected tweet ids for researchers to use.  ...  In addition, we would like to experiment with Deep Learning models and obtain a model that can identify drug related tweets.  ... 
arXiv:2003.13900v1 fatcat:i2pnogxuebauxjkstkj5rh6hyy

An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere [article]

Han Hu and NhatHai Phan and James Geller and Stephen Iezzi and Huy Vo and Dejing Dou and Soon Ae Chun
2019 arXiv   pre-print
As the problem of drug abuse intensifies in the U.S., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use machine learning as  ...  Experiments are reported on a Twitter dataset, where we can configure the percentages of the two classes (abuse vs. non abuse) to simulate the data imbalance with different amplitudes.  ...  [18] performed a comprehensive study on using deep learning models to identify mentions of drug intake in tweets. Katsuki et al.  ... 
arXiv:1904.02062v1 fatcat:vdmfs4rctnde5dbixgbna2i3xy

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  
The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique  ...  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.  ...  ACKNOWLEDGEMENTS The authors would like to thank Dr Karen L. 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

Characterizing drug mentions in COVID-19 Twitter Chatter [article]

Ramya Tekumalla, Juan M. Banda
2020 arXiv   pre-print
In this work, we mined a large twitter dataset of 424 million tweets of COVID-19 chatter to identify discourse around drug mentions.  ...  While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning alongside traditional automated methods to aid in this task.  ...  We would like to thank Stephen Fleischman and HP labs for providing us with server access to perform our experiments during our research server downtime.  ... 
arXiv:2007.10276v2 fatcat:pxafaaq6ojblzhlovx2h3pe6rm

An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning

Han Hu, NhatHai Phan, Soon A. Chun, James Geller, Huy Vo, Xinyue Ye, Ruoming Jin, Kele Ding, Deric Kenne, Dejing Dou
2019 Computational Social Networks  
An insight analysis of drug-abuse risk behavior on Twitter To gain insights in drug-abuse risk behaviors on Twitter, we use our best performing deep self-taught learning model to annotate over three million  ...  [26] demonstrated an attempt to identify emerging drug terms using NLP techniques.  ...  Availability of data and materials The data are available upon request, following the data privacy policy of Twitter. Competing interests The authors declare that they have no competing interests.  ... 
doi:10.1186/s40649-019-0071-4 fatcat:jfnkm7ocafbc7f6w23mevwmkpi

Deep Neural Networks Ensemble for Detecting Medication Mentions in Tweets [article]

Davy Weissenbacher, Abeed Sarker, Ari Klein, Karen O'Connor, Arjun Magge Ranganatha, Graciela Gonzalez-Hernandez
2019 arXiv   pre-print
Conclusion: The system identifies tweets mentioning drug names with performance high enough to ensure its usefulness and ready to be integrated in larger natural language processing systems.  ...  Methods: We present Kusuri, an Ensemble Learning classifier, able to identify tweets mentioning drug products and dietary supplements. Kusuri ("medication" in Japanese) is composed of two modules.  ...  Introduction Twitter has been utilized as an important source of patient-generated data that can provide unique insights into population health [1] .  ... 
arXiv:1904.05308v1 fatcat:dawfve7o7jebzfyvpt67moaor4

Mining social media for prescription medication abuse monitoring: a review and proposal for a data-centric framework

2019 JAMIA Journal of the American Medical Informatics Association  
Early studies focused mostly on manual, qualitative analyses, with a growing trend toward the use of data-centric methods involving natural language processing and machine learning.  ...  The development of reproducible and standardized data-centric frameworks that build on the current state-of-the-art methods in data and text mining may enable effective utilization of social media data  ...  More than 800 keywords were used to collect data, followed by crowd-sourced annotation of 4985 tweets. Deep learning model built on small annotated data and evaluated via 10-fold cross-validation.  ... 
doi:10.1093/jamia/ocz162 pmid:31584645 pmcid:PMC7025330 fatcat:ilk34pcwkjdgvectwvz26hheoq

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.  ...  Experiments are performed on Twitter, PubMed, TwiMed-Twitter, and TwiMed-PubMed datasets.  ...  Using multitask learning, an end-to-end solution for identifying ADRs is presented.  ... 
doi:10.1155/2021/5589829 pmid:34422092 pmcid:PMC8378963 fatcat:p7ryc7zkfbc2jaxlarohpq62g4

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  
This study aimed to demonstrate a quick and accurate method to collect and classify information based on the distribution of approved data on Twitter.  ...  We used deep learning methods with the word2vec to classify ADR comments posted by the users and present an architecture by HAN, FastText, and CNN.  ...  We used two categories of data to detect medication side effects and to generate and analyze combined dataset by deep learning.  ... 
doi:10.22074/cellj.2020.6615 pmid:31863657 pmcid:PMC6947008 fatcat:kizwy4mi7nbflfs37qq56zmouu

Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: A Methodological Review (Preprint)

Tavleen Singh, Kirk Roberts, Trevor Cohen, Nathan Cobb, Jing Wang, Kayo Fujimoto, Sahiti Myneni
2020 JMIR Public Health and Surveillance  
Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies.  ...  ," "machine learning," "data mining," etc.  ...  One study used an ensemble deep learning model consisting of a word-level CNN and a character-level CNN [73] .  ... 
doi:10.2196/21660 pmid:33252345 fatcat:o42mjvsf2rcd7bmm7ozpmfpvta

Twitter Sentiment Analysis with Diabetic Drugs Using Machine Learning Techniques with Glowworm Swarm Optimization Algorithm

S. Radha Priya, Government Arts College(A), Coimbatore
2020 International Journal of Engineering Research and  
Many people use twitter to communicate their side effects/benefits of diabetic medicines. Other people in turn seek these posts to gain feedback regarding their own Adverse Drug Reactions(ADR).  ...  In this paper ML techniques were used in opinion analysis for processing information about ADR on taking diabetic drugs-Metformin(generic and brand name).  ...  Various machine learning algorithms are used to identify the optimal predictor.  ... 
doi:10.17577/ijertv9is070034 fatcat:itdhqz2akfgyxdimpys3jvdaim
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