EAGER: A New Explainable Multi-objective Learning Framework for Personalized Dietary Recommendations against Opioid Misuse and Addiction

EAGER：一种新的可解释的多目标学习框架，用于针对阿片类药物滥用和成瘾的个性化饮食建议

• 批准号: 2334193
• 负责人: Yanfang Ye
• 金额: $ 30万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334193&HistoricalAwards=false
• 关键词: EAGER; New; Explainable; Multi; objective

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.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.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.

由于过去二十年来全国阿片类药物过量死亡人数持续增加，打击阿片类药物危机是一项国家优先事项。虽然药物辅助治疗（MAT）被认为是治疗阿片类药物滥用和成瘾最有效的方法，但在治疗过程中产生的焦虑和抑郁以及各种副作用可能引发阿片类药物复发。除MAT外，膳食营养干预在阿片类药物滥用预防和恢复中也显示出其重要性。然而，关于如何在阿片类药物滥用和成瘾中提供有效且负担得起的个性化饮食营养干预措施的研究尚缺乏。为了弥补这一差距，本项目的目标是设计和开发一个新的可解释的多目标学习框架，根据阿片类药物使用者的特点和情况量身定制个性化饮食建议，以打击阿片类药物滥用和成瘾，从而有助于加强国家公共卫生、安全和福利。该项目的成果，包括开源代码、基准数据和开发模型，将通过演示、出版物和媒体出版社等方式公开访问并广泛分发。有人认为，与类阿片流行病作斗争需要几代人的长期承诺和努力。该项目将通过学生指导和各种K-12外展活动将研究与教育结合起来，培训和教育后代预防和干预阿片类药物滥用和成瘾。该团队还将扩大针对女性和代表性不足群体的计算机参与。通过新颖的学科视角，这个探索性的高风险高回报项目包括三个相互关联的研究组成部分，用于开发一个新的可解释的多目标学习框架，以对抗阿片类药物滥用和成瘾。首先，基于Yelp等在线平台生成的饮食数据，通过解决在线饮食数据的多模态、异质性、噪声和稀疏性问题，团队将开发用于在线阿片类药物用户检测的新型多模态自监督图学习技术，建立第一个大规模、高质量的阿片类药物用户相关饮食基准数据集。其次，由于阿片类药物使用者的复杂特征和情况，为其推荐最佳饮食是一项巨大的挑战，该团队将开发一种新的基于多跳推理的多目标学习算法，结合多种因素（饮食偏好、营养多样性、用户特定条件），为阿片类药物使用者提供个性化的饮食建议。第三，为了进一步提高推荐接受度，团队将设计和开发一种基于推理路径的新型编码器-解码器文本生成模型，为阿片类药物用户提供建议食谱的文本解释。制定的框架将加速个性化饮食营养干预措施，以减少阿片类药物滥用，并有望对解决这一危机产生重大影响。该研究将促进科学理论的发展，并有利于信息集成和信息学领域以及公共卫生、流行病学、社会和行为科学等多学科领域。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。