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.

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.

In this paper, we propose a system to estimate heterogeneous concurrent drug usage effects on overdose estimation, that consists of efficient co-variate selection, sub-group selection, generation of and heterogeneous causal effect estimation. Although, there has been several association studies have been proposed in the state-of-art methods, heterogeneous causal effects have never been studied in concurrent drug usage and drug overdose problem. We apply our framework to answer a critical question, "can concurrent usage of benzodiazepines and opioids has heterogeneous causal effects on opioid overdose epidemic?" Using Truven MarketScan claim data collected from 2001 to 2013 have shown significant promise of our proposed framework's efficacy. Our efficient causal inference model estimated that the causal effect is higher (19%) than the regression studies (15%) to estimate the risks associated with the concurrent usage of opioid and benzodiazepines on opioid overdose.

Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, in this paper, we propose a novel framework named AutoOPU to automatically detect the opioid users from Twitter, which will assist in sharpening our understanding toward the behavioral process of opioid addiction and treatment. In AutoOPU, to model the users and posted tweets as well as their rich relationships, we first introduce a heterogeneous information network (HIN) for representation. Then we use meta-structure based approach to characterize the semantic relatedness over users. Afterwards, we integrate content-based similarity and relatedness depicted by each meta-structure to formulate a similarity measure over users. Further, we aggregate different similarities using multi-kernel learning, each of which is automatically weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the first work to use multi-kernel learning based on meta-structures over HIN for biomedical knowledge mining, especially in drug-addiction domain. Comprehensive experiments on real sample collections from Twitter are conducted to validate the effectiveness of our developed system AutoOPU in opioid user detection by comparisons with other alternative methods.