SMAMS attempts to detect reports of prescription medication abuse from social media data (currently Twitter). It uses natural language processing and machine learning methods.
Prescription Medication (PM) abuse is a major epidemic in the United States, and monitoring and studying the characteristics of the PM abuse problem requires the development of novel approaches. Social media encapsulates an abundance of data about PM abuse from different demographics, but extracting that data and converting it to knowledge requires advanced natural language processing and data-centric artificial intelligence systems. Our proposed social media mining framework will automate the process of big data to knowledge conversion for PM abuse, providing crucial insights to toxicologists about targeted populations and enabling the future development of directed intervention strategies.
Abeed Sarker, Ph.D. (PI)
Graciela Gonzalez-Hernandez, Ph.D. (Senior Co-investigator)
Jeanmarie Perrone, M.D. (Senior Co-investigator)
Haitao Cai (Developer)
Karen O’Connor (Staff Scientist)
Alexis Upshur (Annotator)
Annika DeRoos (Annotator)
Postdoctoral researcher [TBD]
Sarker A, O’Connor K, Ginn R, Scotch M, Smith K, Malone D, Gonzalez G. Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter. Drug Saf. 2016 Mar;39(3):231-40. doi: 10.1007/s40264-015-0379-4.
Sarker A, Graciela Gonzalez, Francis J. DeRoos, Lewis S. Nelson and Jeanmarie Perrone. Toxicovigilance through social media: quantifying abuse-indicating information in Twitter data. Clinical Toxicology (Abstracts). 2018 May; 56(6):454. http://dx.doi.org/10.1080/15563650.2018.1457818
– coming soon
This project is supported by the National Institute on Drug Abuse (NIDA) of the National Institutes of Health (NIH) under grant number R01DA046619. The content is solely the responsibility of the investigators and does not necessarily represent the official views of the National Institutes of Health.