Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42162, first published .
Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Estimating County-Level Overdose Rates Using Opioid-Related Twitter Data: Interdisciplinary Infodemiology Study

Journals

  1. Tang L, Korona-Bailey J, Zaras D, Roberts A, Mukhopadhyay S, Espy S, Walsh C. Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study. JMIR Public Health and Surveillance 2023;9:e45246 View
  2. Al-Hamid A, Tudor C, Assi S. Exploring profile, effects and toxicity of novel synthetic opioids and classical opioids via Twitter: A qualitative study. Emerging Trends in Drugs, Addictions, and Health 2024;4:100139 View
  3. Almeida A, Patton T, Conway M, Gupta A, Strathdee S, Bórquez A. The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review. JMIR Infodemiology 2024;4:e51156 View
  4. Amer M, Gittins R, Millana A, Scheibein F, Ferri M, Tofighi B, Sullivan F, Handley M, Busse A, Ghosh S, Baldacchino A, Tay Wee Teck J. Are treatment services ready for the use of big data analytics and artificial intelligence in managing opioid use disorder? (Preprint). Journal of Medical Internet Research 2024 View
  5. Sato R, Tsuchiya M, Ichiyama R, Hisamura S, Watabe S, Yanagisawa Y, Nishiyama T, Yada S, Aramaki E, Kizaki H, Imai S, Hori S. Analysis of Overdose-related Posts on Social Media. YAKUGAKU ZASSHI 2024;144(12):1125 View