DOD-Explainer: Explainable Drug Overdose Deaths Predictor from Crime and Socioeconomic Data

Alhamadani, Abdulaziz; Sarkar, Shailik; Alkulaib, Lulwah; Lu, Chang-Tien

doi:10.1109/bigdata55660.2022.10021054

Cited by 1 publication

(1 citation statement)

References 39 publications

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“…Finally, two recent works applied ML methods to statistical data. DOD-Explainer was proposed by [29] to predict overdose deaths in the U.S. based on a combination of socioeconomic and crime data and provided explanations for the leading causes of the crisis. Comparably, Islam et al [30] implemented classical ML classifiers on socioeconomic questionnaire data, but they predicted an individual's vulnerability by identifying key risk factors behind substance abuse.…”

Section: Drug Abuse Detectionmentioning

confidence: 99%

The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination

Alhamadani

Sarkar

Behal

et al. 2023

Preprint

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This work is an extension of our previous efforts to combat the drug abuse epidemic which has been on the rise in the past few years []. We expand our developed framework PRISTINE ((opioid crisis detection on reddit)) to investigate the effectiveness of the framework on detecting opioids crisis trends on an expanded dataset from the two subreddits r/dugs and r/opiates. In this endeavor, we demonstrate the effectiveness of utilizing the DQE algorithm in identifying drug-related and evolving drug terms. we conduct comprehensive case studies for the seven drug categories and showcase the most associated keywords for each drug class and their slang/street names. In addition, we provide a case study on one of the most significant opioid crisis contributors to drug overdose deaths in the United States. Our case studies revealed hard-to-find drugrelated terms which we hope to contribute to mitigating this crisis. We additionally include a new analysis to investigate the efficacy of applying PRISTINE in categorizing subreddits into fine-grained drug classes. The new analysis includes a case study that classifies anonymized and lengthy subreddit comments into their correct drug class. The analysis shows the strong performance of PRISTINE and demonstrates that the framework can be applied to a wide range of subreddit comments. We finally include the performed extensive experiments to show the effectiveness of the overall performance of the proposed framework.

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Section: Drug Abuse Detectionmentioning

confidence: 99%