TY - GEN
T1 - Using Innovations in Data Analytics and Smart Technologies to Fight Opioid Overdose Crisis
AU - Zohrabi, Nasibeh
AU - Britz, Jacqueline B.
AU - Krist, Alex H.
AU - Zaman, Mostafa
AU - Abdelwahed, Sherif
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85169474930&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85169474930&partnerID=8YFLogxK
U2 - 10.1109/SMARTCOMP58114.2023.00052
DO - 10.1109/SMARTCOMP58114.2023.00052
M3 - Conference contribution
AN - SCOPUS:85169474930
T3 - Proceedings - 2023 IEEE International Conference on Smart Computing, SMARTCOMP 2023
SP - 216
EP - 218
BT - Proceedings - 2023 IEEE International Conference on Smart Computing, SMARTCOMP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Smart Computing, SMARTCOMP 2023
Y2 - 26 June 2023 through 29 June 2023
ER -