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)
Anahita Davoudi (Postdoctoral Researcher)
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
Sarker A, Gonzalez-Hernandez G. An unsupervised and customizable misspelling generator for mining noisy health-related text sources. J Biomed Inform. 2018 Dec;88:98-107. doi: 10.1016/j.jbi.2018.11.007. Epub 2018 Nov 13.
Medication Misspelling Generation Tool: available here.
Prescription Medication Abuse Annotation Guidelines: [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.