The opioid epidemic, referring to the growing hospitalizations and deaths
because of overdose of opioid usage and addiction, has become a severe health
problem in the United States. Many strategies have been developed by the
federal and local governments and health communities to combat this crisis.
Among them, improving our understanding of the epidemic through better health
surveillance is one of the top priorities. In addition to direct testing,
machine learning approaches may also allow us to detect opioid users by
analyzing data from social media because many opioid users may choose not to do
the tests but may share their experiences on social media anonymously. In this
paper, we take advantage of recent advances in machine learning, collect and
analyze user posts from a popular social network Reddit with the goal to
identify opioid users. Posts from more than 1,000 users who have posted on
three sub-reddits over a period of one month have been collected. In addition
to the ones that contain keywords such as opioid, opiate, or heroin, we have
also collected posts that contain slang words of opioid such as black or
chocolate. We apply an attention-based bidirectional long short memory model to
identify opioid users. Experimental results show that the approaches
significantly outperform competitive algorithms in terms of F1-score.
Furthermore, the model allows us to extract most informative words, such as
opiate, opioid, and black, from posts via the attention layer, which provides
more insights on how the machine learning algorithm works in distinguishing
drug users from non-drug users.

通过对 Reddit 上的用户帖子进行收集和分析，结合关注度双向长短期记忆模型，该研究利用机器学习技术成功识别出了滥用阿片类药物的用户，并通过关注层提取了关键词，从而更好地理解该算法如何区分药物使用者和非药物使用者。

通过基于注意力的双向递归神经网络从 Reddit 帖子检测阿片类药物用户

Detection of Opioid Users from Reddit Posts via an Attention-based  Bidirectional Recurrent Neural Network

The United States is in the midst of an opioid epidemic with recent estimates
indicating that more than 130 people die every day due to drug overdose. The
over-prescription and addiction to opioid painkillers, heroin, and synthetic
opioids, has led to a public health crisis and created a huge social and
economic burden. Statistical learning methods that use data from multiple
clinical centers across the US to detect opioid over-prescribing trends and
predict possible opioid misuse are required. However, the semantic
heterogeneity in the representation of clinical data across different centers
makes the development and evaluation of such methods difficult and non-trivial.
We create the Opioid Drug Knowledge Graph (ODKG) -- a network of opioid-related
drugs, active ingredients, formulations, combinations, and brand names. We use
the ODKG to normalize drug strings in a clinical data warehouse consisting of
patient data from over 400 healthcare facilities in 42 different states. We
showcase the use of ODKG to generate summary statistics of opioid prescription
trends across US regions. These methods and resources can aid the development
of advanced and scalable models to monitor the opioid epidemic and to detect
illicit opioid misuse behavior. Our work is relevant to policymakers and pain
researchers who wish to systematically assess factors that contribute to opioid
over-prescribing and iatrogenic opioid addiction in the US.

本论文描述了使用药物知识图谱（ODKG）来归一化多个临床数据中的药物命名，以监测美国不同地区的阿片类药物处方趋势，并有助于制定先进和可扩展的模型，以监测阿片类药物滥用行为。

一种基于知识图谱的探索美国阿片类药物流行病的方法

A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic

The dearth of prescribing guidelines for physicians is one key driver of the
current opioid epidemic in the United States. In this work, we analyze medical
and pharmaceutical claims data to draw insights on characteristics of patients
who are more prone to adverse outcomes after an initial synthetic opioid
prescription. Toward this end, we propose a generative model that allows
discovery from observational data of subgroups that demonstrate an enhanced or
diminished causal effect due to treatment. Our approach models these
sub-populations as a mixture distribution, using sparsity to enhance
interpretability, while jointly learning nonlinear predictors of the potential
outcomes to better adjust for confounding. The approach leads to
human-interpretable insights on discovered subgroups, improving the practical
utility for decision support

本研究提出了一种生成模型，通过分析医药索赔数据来发现对患者造成不良后果的特点，以便解决目前美国合成阿片类药物处方滥用的问题。