Mining Social Media Big Data for Toxicovigilance: Studying Substance Use via Natural Language Processing and Machine Learning Methods

挖掘社交媒体大数据进行毒物警戒：通过自然语言处理和机器学习方法研究药物使用

• 批准号: 10588855
• 负责人: Abeed H Sarker
• 金额: $ 127.24万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10588855
• 关键词: Address; Advanced Development; American; Artificial Intelligence; Barbiturates; Big Data; Big Data to Knowledge; COVID-19 pandemic; Cessation of life; Classification; Collection; Complement; Complex; Data; Data Aggregation; Data Collection; Data Set; Death Rate; Detection; Development; Drug Prescriptions; Elements; Emergency department visit; Encapsulated; Epidemic; Evaluation; Evolution; Expert Systems; Fentanyl; Funding; Future; Gender; Gender Identity; Generations; Heroin; Human; Illicit Drugs; Infrastructure; Intervention; Knowledge; Longitudinal trends; Machine Learning; Manuals; Measures; Methamphetamine; Methodology; Methods; Mining; Names; National Institute of Drug Abuse; Natural Language Processing; Observational Study; Online Systems; Outcome; Overdose; Patient Self-Report; Pattern; Persons; Pharmaceutical Preparations; Policies; Population; Process; Publications; Race; Reporting; Research; Resources; Source; Source Code; Stigmatization; Substance Use Disorder; Supervision; Surveys; Target Populations; Time; TimeLine; Twitter; Uninsured; United States; Validation; Variant; Work; Xylazine; age group; analog; base; cohort; cost; dashboard; data access; data mining; data tools; data visualization; detection method; ethnic minority; evidence base; experience; improved; innovation; insight; interest; learning strategy; longitudinal analysis; machine learning method; novel; novel strategies; open source; open source tool; opioid epidemic; overdose death; preference; psychostimulant; racial and ethnic; real time monitoring; response; social media; social stigma; spelling; statistics; substance use; surveillance strategy; synthetic opioid; treatment disparity; trend; trend analysis

The epidemic of substance use (SU) and substance use disorder (SUD) in the United States has been evolving for decades. Both prescription and illicit drugs have been involved in overdose deaths over the years, with notable increases in synthetic opioids (eg., fentanyl & analogs) and psychostimulants (eg., methamphetamine) in recent years. The emergence of high-potency novel psychoactive substances (NPSs), such as fentanyl analogs, have drastically contributed to rising deaths, and adversely impacted treatment engagement and response. The COVID19 pandemic has further exacerbated the crisis, and recent studies have also highlighted that substantial disparities exist in SUD treatment, research, interest, and response across different subpopulations, with racial/ethnic minorities being disproportionately impacted. A key element to tackling the crisis is improved surveillance. Specifically, there is a need for establishing novel approaches to provide timely insights about the trends, distributions, and trajectories of the SUD epidemic, as traditional surveillance approaches involve considerable lags. Many recent studies have identified social media (SM) as useful resources for conducting SU/SUD surveillance. Many people use SM to discuss personal experiences, provide advice, or seek answers to questions regarding SU/SUD, resulting in the generation of an abundance of information. Such information can be characterized, aggregated and analyzed to obtain population- or subpopulation-level insights, at low cost and in near real time. However, converting SM data into timely, actionable knowledge is non-trivial since the data is big, complex, and noisy, requiring the development of advanced, automated artificial intelligence methods. Funded by the National Institute on Drug Abuse, our past work focused specifically on prescription medications (PM) and established the most sophisticated SM-based data mining pipeline available to date. In response to the evolution of the SUD epidemic, the proposed project will extend our capabilities to include illicit substances and develop novel methods to conduct surveillance. Specifically, we will (i) extend our machine learning and natural language processing (NLP) classification pipeline to automatically classify all SU-related chatter from Twitter and Reddit (rather than PMs only), (ii) collect and analyze longitudinal timelines of cohorts self-reporting SU/SUD, (iii) characterize the cohorts in terms of demographic details such as age-group, gender identity, race and geolocation, (iv) develop advanced NLP-driven methods for detecting NPSs and impacts of SU/SUD, (v) study short-term and long-term trends and trajectories of the epidemic, (vi) conduct observational studies on targeted population subsets, including studies focusing on SU and SUD treatment disparities and stigma, and (vii) disseminate developed methodologies via open source code and aggregated findings publicly via a web- based dashboard. Implementation of our data-centric methods and successful execution of the project has the potential to transform SU/SUD surveillance, and complement traditional surveillance measures by providing close to real time statistics and insights, including those for targeted subpopulations.

美国的流行病（SU）和药物使用障碍（SUD）正在发展 几十年。多年来，处方药和非法药物都涉及过量死亡，显着 最近的合成阿片类药物（例如芬太尼和类似物）和精神刺激剂（例如，甲基苯丙胺）的增加 年。高功能新颖的精神活性物质（NPS）的出现，例如芬太尼类似物 急剧导致死亡的增加，并对治疗的参与和反应产生不利影响。这 Covid19大流行进一步加剧了危机，最近的研究也强调了这一点 在不同亚种群中，SUD治疗，研究，兴趣和反应中存在差异， 种族/族裔少数民族受到过度影响。解决危机的关键要素得到了改善 监视。具体而言，需要建立新颖的方法来提供有关及时的见解 由于传统的监视方法涉及SUD流行的趋势，分布和轨迹 相当大的滞后。许多最近的研究都将社交媒体（SM）确定为进行的有用资源 SU/SUD监视。许多人使用SM讨论个人经验，提供建议或寻求答案 有关SU/SUD的问题，导致大量信息产生。这样的信息可以 以低成本和 接近实时。但是，将SM数据转换为及时的，可行的知识是非平凡的，因为数据是 大型，复杂且嘈杂，需要开发高级自动人工智能方法。 由美国国家药物滥用研究所资助，我们过去的工作专门针对处方药 （PM）并建立了迄今为止可用的最复杂的基于SM的数据挖掘管道。回应 SUD流行的进化，拟议项目将扩展我们的能力，以包括非法物质和 开发进行监视的新方法。具体来说，我们将（i）扩展机器学习和自然 语言处理（NLP）分类管道将自动从Twitter分类所有与SU相关的聊天 和reddit（而不是仅PMS），（ii）收集和分析同类的纵向时间表自我报告 su/sud，（iii）以人口统计细节（例如年龄段，性别认同，种族）来表征人群 和地理位置，（iv）开发了用于检测NPS和SU/SUD的影响的先进NLP驱动的方法，（V） 研究流行病的短期和长期趋势和轨迹，（vi）进行观察性研究 有针对性的人群子集，包括关注SU和SUD治疗差异和污名的研究，以及 （vii）通过开源代码传播开发的方法论，并通过网络公开汇总发现 基于仪表板。我们以数据为中心的方法的实施和项目的成功执行具有 通过提供的潜力改变SU/SUD监视，并通过提供传统监视措施来补充传统的监视措施 接近实时统计和见解，包括针对目标亚群的统计数据。