Using Innovations in Data Analytics and Smart Technologies to Fight Opioid Overdose Crisis

Nasibeh Zohrabi, Jacqueline B. Britz, Alex H. Krist, Mostafa Zaman, Sherif Abdelwahed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Drug overdose is now the leading cause of death for those under 50 in the United States. Inadequate data present a challenge for city officials, which prevents them from investigating the scale of the opioid overdose crisis. Various factors need to be considered in the prediction model for estimating the level of drug consumption, type of drug, and the location of the affected area. The aim of this project is to investigate several prediction and analysis models for forecasting drug use and overdoses by considering diverse data obtained from different sources, including sewage-based drug epidemiology, healthcare data, social networks data mining, and police data. Such analysis will help to formulate more effective policies and programs to combat fatal opioid overdoses.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE International Conference on Smart Computing, SMARTCOMP 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages216-218
Number of pages3
ISBN (Electronic)9798350322811
DOIs
StatePublished - 2023
Event9th IEEE International Conference on Smart Computing, SMARTCOMP 2023 - Nashville, United States
Duration: Jun 26 2023Jun 29 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Smart Computing, SMARTCOMP 2023

Conference

Conference9th IEEE International Conference on Smart Computing, SMARTCOMP 2023
Country/TerritoryUnited States
CityNashville
Period6/26/236/29/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this