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

TL;DR

This paper introduces Diet-ODIN, a novel framework combining heterogeneous graph learning and large language models to detect opioid misuse and interpret associated dietary patterns, addressing a critical gap in understanding diet's role in opioid addiction.

Contribution

The paper develops a large-scale dietary benchmark dataset and proposes Diet-ODIN, a new interpretable framework that integrates graph learning and LLMs for opioid misuse detection and dietary pattern analysis.

Findings

Diet-ODIN outperforms baseline methods in detection accuracy.

The framework effectively interprets dietary patterns linked to opioid misuse.

Experimental results validate the model's ability to explore diet-opioid misuse relationships.

Abstract

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.