Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access.

Journal: Journal of medical Internet research

PMID: 29613851

Abstract

BACKGROUND: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track.

Authors

Tim Mackey

Division of Infectious Disease and Global Public Health, Department of Anesthesiology, School of Medicine, University of California San Diego, La Jolla, CA, United States.
Janani Kalyanam

Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, USA.
Josh Klugman

IBM Global Business Services, Washington, DC, United States.
Ella Kuzmenko

IBM Global Business Services, Washington, DC, United States.
Rashmi Gupta

Centers for Disease Control and Prevention, Atlanta, GA, United States.

Keywords

Analgesics, Opioid Controlled Substances Humans Internet Machine Learning Marketing Pharmaceutical Services, Online Prescription Drug Misuse Social Media

External Resources

View on PubMed Access via DOI PubMed (29613851)

Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access.

Abstract

Authors

Keywords

External Resources

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