Predictive Modelling to Identify Risk Factors Leading to Opioid Addiction

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Last Modified
  • February 28, 2019
Creator
  • Shah, Nidhi
    • Affiliation: School of Information and Library Science
Abstract
  • Prescription Opioid Abuse continues to be a significant and growing problem in the United States. According to the National Institute of Drug Abuse, more than 115 Americans die every day from an opioid overdose. Considering the graveness of the situation, this project aims at briefly studying the current trends and literature pertaining to the opioid epidemic and coming up with preventive measures. This was done by studying prevalent literature and data available on this subject to analyze patterns and trends of opioid addiction to help identify risk factors, using predictive modelling.
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Advisor
  • Gotz, David
Degree
  • Master of Science in Information Science
Academic concentration
  • Library and Information Science
Degree granting institution
  • University of North Carolina at Chapel Hill
Graduation year
  • 2018
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Deposit record
  • 55971047-5ee4-46a3-a494-995ed360088b

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