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A Data-Driven Method of Discovering Misspellings of Medication Names on Twitter

Keyuan Jiang, Tingyu Chen, Liyuan Huang, Ricardo A Calix, Gordon R Bernard
2018 Studies in Health Technology and Informatics  
We developed a data-driven, relational similarity-based approach to discover misspellings of medication names.  ...  Compared with the phonetics-based approach, our method discovered more actual misspellings used on Twitter.  ...  This work was supported by the National Institutes of Health Grant 1R15LM011999-01.  ... 
pmid:29677938 pmcid:PMC6009827 fatcat:cmaxkf73mnchxjadglwkvyrnmy

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
Given that lexical searches for medication names may fail due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them.  ...  On a corpus made of all tweets posted by 113 Twitter users (98,959 tweets, with only 0.26% mentioning medications), Kusuri obtained 76.3% F1-score.  ...  Variant-based Drug Classifier Names of drugs may have a complex morphology and, as a consequence, are often misspelled on Twitter.  ... 
arXiv:1904.05308v1 fatcat:dawfve7o7jebzfyvpt67moaor4

An unsupervised and customizable misspelling generator for mining noisy health-related text sources

Abeed Sarker, Graciela Gonzalez-Hernandez
2018 Journal of Biomedical Informatics  
The performance and relative simplicity of our proposed approach make it a much-needed spelling variant generation resource for health-related text mining from noisy sources.  ...  Acknowledgments Research performed for this publication by Abeed Sarker was supported by the National Institute on Drug Abuse (NIDA) of the National Institutes of Health under Award Number R01DA046619.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.  ... 
doi:10.1016/j.jbi.2018.11.007 pmid:30445220 pmcid:PMC6322919 fatcat:jxoqqskvdvavth2c3a6mdtvdiu

An unsupervised and customizable misspelling generator for mining noisy health-related text sources [article]

Abeed Sarker, Graciela Gonzalez-Hernandez
2018 arXiv   pre-print
On a dataset prepared for this study, our system outperforms the current state-of-the-art for medication name variant generation with best F1-score of 0.69 and F1/4-score of 0.78.  ...  Extrinsic evaluation of the system on a set of cancer-related terms showed an increase of over 67% in retrieval rate from Twitter posts when the generated variants are included.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the NLM or NIH.  ... 
arXiv:1806.00910v1 fatcat:adxxsh6usrealarla3rwcrqwa4

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  
Prescription medication (PM) misuse and abuse is a major health problem globally, and a number of recent studies have focused on exploring social media as a resource for monitoring nonmedical PM use.  ...  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  ...  The data collection strategy has to take into account common misspellings, 41 and street names for medications, as many abuse-prone medications have commonly used street names (eg, "oxy," "percs," "addy  ... 
doi:10.1093/jamia/ocz162 pmid:31584645 pmcid:PMC7025330 fatcat:ilk34pcwkjdgvectwvz26hheoq

Towards Automating Location-Specific Opioid Toxicosurveillance from Twitter via Data Science Methods

Abeed Sarker, Graciela Gonzalez-Hernandez, Jeanmarie Perrone
2019 Studies in Health Technology and Informatics  
Our objectives for this study were to (i) manually characterize a sample of opioid-mentioning Twitter posts, (ii) compare the rates of abuse/misuse related posts between prescription and illicit opiods  ...  Lack of context in tweets and data imbalance resulted in misclassification of many tweets to the majority class.  ...  The data collection and annotation efforts were partly funded by a grant from the Pennsylvania Department of Health. The Titan Xp used for this research was donated by the NVIDIA Corporation.  ... 
doi:10.3233/shti190238 pmid:31437940 pmcid:PMC6774610 fatcat:gqz7ujnk4bcidjli4hfsi7tmoq

RedMed: Extending drug lexicons for social media applications [article]

Adam Lavertu, Russ B Altman
2019 bioRxiv   pre-print
We provide a lexicon of misspellings and synonyms for 2,978 drugs and a word embedding model trained on a health-oriented subset of Reddit.  ...  Of all drug mentions, an average of 79% (±0.5%) were exact matches to a generic or trademark drug name, 14% (±0.5%) were misspellings, 6.4% (±0.3%) were synonyms, and 0.13% (±0.05%) were pill marks.  ...  Al-526 though our model is trained on non-technical documents, it performs well on several benchmark medical similarity tasks. We used our method to identify drug misspellings and syn-528 onyms.  ... 
doi:10.1101/663625 fatcat:rnkr5hquaveffjxfdhsrihkfiy

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
2017 IMIA Yearbook of Medical Informatics  
On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to  ...  Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health.  ... 
doi:10.1055/s-0037-1606506 fatcat:ujcstkryrzhgpalibaaduzchfy

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
2017 IMIA Yearbook of Medical Informatics  
On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to  ...  Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health.  ... 
doi:10.15265/iy-2017-029 pmid:29063568 pmcid:PMC6250990 fatcat:i6kvjcddgnbfvojmloral5pncu

Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter

Abeed Sarker, Karen O'Connor, Rachel Ginn, Matthew Scotch, Karen Smith, Dan Malone, Graciela Gonzalez
2016 Drug Safety  
We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse.  ...  Methods We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall Ò , oxycodone, and quetiapine).  ...  use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes  ... 
doi:10.1007/s40264-015-0379-4 pmid:26748505 pmcid:PMC4749656 fatcat:y5u3v2tgjjehheeqbtxzyco7su

Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource

Abeed Sarker, Sahithi Lakamana, Whitney Hogg-Bremer, Angel Xie, Mohammed Ali Al-Garadi, Yuan-Chi Yang
2020 JAMIA Journal of the American Medical Informatics Association  
Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies.  ...  Materials and Methods We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users.  ...  MATERIALS AND METHODS Data collection and user selection We collected tweets, including texts and metadata, from Twitter via its public streaming application programming interface.  ... 
doi:10.1093/jamia/ocaa116 pmid:32620975 fatcat:nlsj54ozy5bltgrgtwoksoc2tu

Neural attention with character embeddings for hay fever detection from twitter

Jiahua Du, Sandra Michalska, Sudha Subramani, Hua Wang, Yanchun Zhang
2019 Health Information Science and Systems  
We propose a deep architecture enhanced with character embeddings and neural attention to improve the performance of hay fever-related content classification from Twitter data.  ...  As a result, the detection of actual hay fever instances among the number of false positives, as well as the correct identification of non-technical expressions as pollen allergy symptoms poses a major  ...  Twitter data has been proven to be a valuable source of information on emerging symptoms as well as treatments usage from directly affected individuals.  ... 
doi:10.1007/s13755-019-0084-2 pmid:31656594 pmcid:PMC6790203 fatcat:sih35mxanvdorf4nbclkwz2kvu

Rumor Detection on Twitter with Hierarchical Attention Neural Networks

Zengrong Guo, Juan Yang
2018 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS)  
We propose a deep architecture enhanced with character embeddings and neural attention to improve the performance of hay fever-related content classification from Twitter data.  ...  As a result, the detection of actual hay fever instances among the number of false positives, as well as the correct identification of non-technical expressions as pollen allergy symptoms poses a major  ...  Twitter data has been proven to be a valuable source of information on emerging symptoms as well as treatments usage from directly affected individuals.  ... 
doi:10.1109/icsess.2018.8663917 fatcat:phrckvekpveq5d6pfk7fhxlyzq

Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task

2018 JAMIA Journal of the American Medical Informatics Association  
We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification  ...  An additional objective was to publicly release manually annotated data.  ...  Domain expertize for the University of Manchester team was provided by Professor William G. Dixon, Director of the Arthritis Research U.K. Centre for Epidemiology.  ... 
doi:10.1093/jamia/ocy114 pmid:30272184 pmcid:PMC6188524 fatcat:xcnb65ojmvbh7e55ylxhsnusrm

Quantifying Self-Reported Adverse Drug Events on Twitter

Vassilis Plachouras, Jochen L. Leidner, Andrew G. Garrow
2016 Proceedings of the 7th 2016 International Conference on Social Media & Society - SMSociety '16  
Materials and methods We retrieved tweets using COVID-19-related keywords, and performed several layers of semi-automatic filtering to curate self-reports of positive-tested users.  ...  Mild symptoms, such as anosmia (26%) and ageusia (24%) were frequently reported on Twitter, but not in clinical studies.  ...  MATERIALS AND METHODS Data collection and user selection We collected data from Twitter via the twitter public streaming application programming interface (API).  ... 
doi:10.1145/2930971.2930977 dblp:conf/smsociety/PlachourasLG16 fatcat:ki5y2nuasrdabeu3oc5e3xgu3a
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