  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.
