The opioid crisis has been one of the most critical society concerns in the
United States. Although the medication assisted treatment (MAT) is recognized
as the most effective treatment for opioid misuse and addiction, the various
side effects can trigger opioid relapse. In addition to MAT, the dietary
nutrition intervention has been demonstrated its importance in opioid misuse
prevention and recovery. However, research on the alarming connections between
dietary patterns and opioid misuse remain under-explored. In response to this
gap, in this paper, we first establish a large-scale multifaceted dietary
benchmark dataset related to opioid users at the first attempt and then develop
a novel framework - i.e., namely Opioid Misuse Detection with Interpretable
Dietary Patterns (Diet-ODIN) - to bridge heterogeneous graph (HG) and large
language model (LLM) for the identification of users with opioid misuse and the
interpretation of their associated dietary patterns. Specifically, in
Diet-ODIN, we first construct an HG to comprehensively incorporate both dietary
and health-related information, and then we devise a holistic graph learning
framework with noise reduction to fully capitalize both users' individual
dietary habits and shared dietary patterns for the detection of users with
opioid misuse. To further delve into the intricate correlations between dietary
patterns and opioid misuse, we exploit an LLM by utilizing the knowledge
obtained from the graph learning model for interpretation. The extensive
experimental results based on our established benchmark with quantitative and
qualitative measures demonstrate the outstanding performance of Diet-ODIN in
exploring the complex interplay between opioid misuse and dietary patterns, by
comparison with state-of-the-art baseline methods.

通过构建大规模的多方面的与阿片类药物使用者相关的膳食基准数据集，并借助 Opioid Misuse Detection with Interpretable Dietary Patterns (Diet-ODIN) 框架，该论文在探索阿片类药物滥用与饮食模式之间的复杂相互作用方面表现出杰出的性能。

Diet-ODIN: 可解释的饮食模式用于防止滥用阿片药物的新框架

Diet-ODIN: A Novel Framework for Opioid Misuse Detection with  Interpretable Dietary Patterns

Opioid and substance misuse is rampant in the United States today, with the
phenomenon known as the "opioid crisis". The relationship between substance use
and mental health has been extensively studied, with one possible relationship
being: substance misuse causes poor mental health. However, the lack of
evidence on the relationship has resulted in opioids being largely inaccessible
through legal means. This study analyzes the substance use posts on social
media with opioids being sold through crypto market listings. We use the Drug
Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based
models to generate sentiment and emotion for the social media posts to
understand users' perceptions on social media by investigating questions such
as: which synthetic opioids people are optimistic, neutral, or negative about?
or what kind of drugs induced fear and sorrow? or what kind of drugs people
love or are thankful about? or which drugs people think negatively about? or
which opioids cause little to no sentimental reaction. We discuss how we
crawled crypto market data and its use in extracting posts for fentanyl,
fentanyl analogs, and other novel synthetic opioids. We also perform topic
analysis associated with the generated sentiments and emotions to understand
which topics correlate with people's responses to various drugs. Additionally,
we analyze time-aware neural models built on these features while considering
historical sentiment and emotional activity of posts related to a drug. The
most effective model performs well (statistically significant) with
(macroF1=82.12, recall =83.58) to identify substance use disorder.

本文使用深度学习、BERT 模型以及情感分析来研究社交媒体中与合成类阿片类药物相关的帖子，分析了社交媒体和加密市场数据，探究人们对不同药物的感受和态度，最终建立了基于这些特征的时间感知神经模型，成功地 (具有统计学意义) 识别出物质滥用障碍。

我们能检测出物质使用障碍吗？：来自暗网的社交媒体知识与时间感知分类

"Can We Detect Substance Use Disorder?": Knowledge and Time Aware  Classification on Social Media from Darkweb

Opioid and substance misuse is rampant in the United States today, with the
phenomenon known as the opioid crisis. The relationship between substance use
and mental health has been extensively studied, with one possible relationship
being substance misuse causes poor mental health. However, the lack of evidence
on the relationship has resulted in opioids being largely inaccessible through
legal means. This study analyzes the substance misuse posts on social media
with the opioids being sold through crypto market listings. We use the Drug
Abuse Ontology, state-of-the-art deep learning, and BERT-based models to
generate sentiment and emotion for the social media posts to understand user
perception on social media by investigating questions such as, which synthetic
opioids people are optimistic, neutral, or negative about or what kind of drugs
induced fear and sorrow or what kind of drugs people love or thankful about or
which drug people think negatively about or which opioids cause little to no
sentimental reaction. We also perform topic analysis associated with the
generated sentiments and emotions to understand which topics correlate with
people's responses to various drugs. Our findings can help shape policy to help
isolate opioid use cases where timely intervention may be required to prevent
adverse consequences, prevent overdose-related deaths, and worsen the epidemic.

该研究分析了社交媒体上的药物滥用帖子和通过加密市场公开售卖的合成阿片类药物，并使用药物滥用本体论，深度学习和基于 BERT 的模型来生成情感和情绪，进而了解用户对各种药物的感知，核对各个药物对应的情绪和情感，结合话题分析帮助政策制定，在必要时进行干预以遏制阿片类药物的危害。