Detecting Anomalous Networks of Opioid Prescribers and Dispensers in Prescription Drug Data

نویسندگان

چکیده

The opioid overdose epidemic represents a serious public health crisis, with fatality rates rising considerably over the past several years. To help address abuse of prescription opioids, state governments collect data on dispensed prescriptions, yet use these is typically limited to manual searches. In this paper, we propose novel graph-based framework for detecting anomalous prescribing patterns in Prescription Drug Monitoring Program (PDMP) data, which could aid deterring diversion and abuse. Specifically, seek identify connected networks prescribers dispensers who engage high-risk possibly illicit activity. We develop apply extension Non-Parametric Heterogeneous Graph Scan (NPHGS) two years de-identified PDMP from Kansas, find that NPHGS identifies subgraphs are significantly more than those detected by other methods. also reveals clusters potentially activity, may strengthen law enforcement regulatory capabilities. Our paper first demonstrate how can systematically dispensers, as well illustrating efficacy network-based approach. Additionally, our technical extensions offer both improved flexibility graph density reduction, enabling be replicated across jurisdictions extended problem domains.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2023

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v37i12.26692