NSF Award
2334193
As opioid overdose deaths have continued to increase over the past two decades across the country, combating the opioid crisis is a national priority. Although medication assisted treatment (MAT) is recognized as the most effective treatment for opioid misuse and addiction, the anxiety and depression created during the treatment and various side effects can trigger opioid relapse. In addition to MAT, dietary nutrition intervention has demonstrated its importance in opioid misuse prevention and recovery. However, research on how to provide effective yet affordable personalized dietary nutrition interventions in opioid misuse and addiction is lacking. To bridge this gap, the goal of this project is to design and develop a new explainable multi-objective learning framework for personalized dietary recommendations tailored to opioid users' characteristics and circumstances to combat opioid misuse and addiction, and thus help enhance national public health, safety, and welfare. The outcomes from this project, including open-source code, benchmark data, and developed models, will be made publicly accessible and broadly distributed through demos, publications, and media presses, etc. It has been argued that combating the opioid epidemic will take long-term commitment and effort for several generations. This project will integrate research with education via student mentoring and various K-12 outreach activities to train and educate future generations in the prevention and intervention of opioid misuse and addiction. The team will also broaden participation in computing aiming at women and underrepresented groups.<br/><br/>By engaging novel disciplinary perspectives, this exploratory high risk-high payoff project includes three interconnected research components for the development of a new explainable multi-objective learning framework to combat opioid misuse and addiction. First, based on the dietary data generated from the online platforms such as Yelp, by addressing the issues of multi-modality, heterogeneity, noise and sparseness of the online dietary data, the team will develop novel multi-modal self-supervised graph learning techniques for online opioid user detection to establish the first large-scale, high-quality, opioid-user-related dietary benchmark dataset. Second, as it is a great challenge to recommend optimal diets for opioid users due to their complex characteristics and circumstances, the team will develop a new multi-objective learning algorithm based on multi-hop reasoning to incorporate multiple factors (diet preference, nutrient diversity, user-specific condition) for personalized dietary recommendations to opioid users. Third, to further promote recommendation receptivity, the team will design and develop a novel encoder-decoder text generation model based on the reasoning paths to provide opioid users with textual explanations of suggested recipes. The developed framework will accelerate personalized dietary nutrition interventions for reducing opioid misuse, and is expected to have a significant impact on addressing this crisis. The research will advance scientific theory and benefit the information integration and informatics domain as well as multidisciplinary areas such as public health, epidemiology, and social and behavioral sciences.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
