A Study of Social Web Data on Buprenorphine Abuse Using Semantic Web Technology

利用语义网技术研究丁丙诺啡滥用的社交网络数据

• 批准号: 8269958
• 负责人: Raminta Daniulaityte
• 金额: $ 18.25万
• 依托单位: WRIGHT STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8269958
• 关键词: Accident and Emergency department; Archives; Area; Behavior; Buprenorphine; Code; Collaborations; Communities; Complement; Complex; Data; Data Sources; Drug Rehabilitation Centers; Drug abuse; Drug usage; Drug user; Early identification; Epidemiologic Monitoring; Epidemiologic Studies; Epidemiology; Face; Health; Health Knowledge, Attitudes, Practice; Health Professional; Heroin Dependence; Hospitals; Illicit Drugs; Individual; Information Sciences; Information Services; Information Systems; Internet; Intervention; Intervention Studies; Interview; Knowledge; Label; Language; Link; Machine Learning; Manuals; Measures; Methods; Monitor; NIH Program Announcements; Naloxone; Natural Language Processing; Online Systems; Opioid; Overdose; Pathway interactions; Pattern; Pharmaceutical Preparations; Pharmacologic Substance; Poison Control Centers; Policies; Population; Prevention; Principal Investigator; Process; Public Health; Published Comment; Qualitative Research; Reading; Reliance; Research; Research Methodology; Research Personnel; Sampling; Selection Bias; Self Disclosure; Semantics; Source; Substance abuse problem; Subutex; Surveillance Methods; Surveys; System; Techniques; Technology; Text; Time; Universities; addiction; buprenorphine abuse; computer based Semantic Analysis; design; emotional disclosure; empowered; experience; informant; information processing; misuse of prescription only drugs; population survey; post-market; prescription drug abuse; programs; response; social; tool; trend; web site; working group

DESCRIPTION (provided by applicant): The non-medical use of pharmaceutical opioids has been identified as one of the fastest growing forms of drug abuse in the U.S. There is a critical need to enhance current epidemiological monitoring, early warning, and post-marketing surveillance systems by providing additional and more timely data. The World Wide Web has been identified as one of the "leading edge" data sources for detecting patterns and changes in drug use practices. Many websites provide a venue for individuals to freely share their own experiences, post questions, and offer comments about different drugs. Such User Generated Content (UGC) can be used as a very rich data source to study knowledge, attitudes, and behaviors related to illicit drugs. To harness the full potential of the Web for drug abuse research, the field needs to develop a highly automated way of accessing, extracting, and analyzing Web-based data related to illicit drug use. This exploratory R21 is a multi-principal investigator, collaborative effort between researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) and the Center for Knowledge-Enabled Information Services and Science (Kno.e.sis) at Wright State University. The purpose of this Web-based study is to apply cutting-edge information processing techniques, such as the Semantic Web, Natural Language Processing, and Machine Learning, to qualitative and quantitative content analysis of user generated content to achieve the following aims: 1) Describe drug users' knowledge, attitudes, and behaviors related to the illicit use of Suboxone(R) (buprenorphine/naloxone) and Subutex(R) (buprenorphine); 2) Identify and describe temporal patterns of the illicit use of these drugs as reflected on web-based forums. To collect data, the study will use websites that allow for the free discussion of illicit drugs, contain information on illicit prescription drug use, and are accessible for public viewing. The study will generate new information about the practices of buprenorphine abuse and will contribute to the advancement of public health and substance abuse research by providing automatic coding and information extraction tools needed to handle rapidly growing Web-based data. Automated information extraction methods applied in this study will enhance current early warning and epidemiological surveillance systems and could advance qualitative and Web-based research methods in other areas of public health.

描述（由申请人提供）：阿片类药物的非医疗用途已被确定为美国增长最快的药物滥用形式之一。迫切需要加强当前的流行病学监测、早期预警和上市后监测系统通过提供额外的和更及时的数据。万维网已被确定为检测吸毒行为模式和变化的“前沿”数据源之一。许多网站为个人提供了一个自由分享自己的经验、发布问题并提供有关不同药物的评论的场所。此类用户生成内容（UGC）可以作为非常丰富的数据源来研究与非法药物相关的知识、态度和行为。为了充分利用网络进行药物滥用研究的潜力，该领域需要开发一种高度自动化的方法来访问、提取和分析与非法药物使用相关的基于网络的数据。这项探索性的 R21 是莱特州立大学干预、治疗和成瘾研究中心 (CITAR) 和知识支持信息服务和科学中心 (Kno.e.sis) 的研究人员共同努力的多学科研究者的合作成果。这项基于网络的研究的目的是应用尖端的信息处理技术，如语义网、自然语言处理和机器学习，对用户生成的内容进行定性和定量的内容分析，以实现以下目标：1)描述吸毒者与非法使用 Suboxone(R)（丁丙诺啡/纳洛酮）和 Subutex(R)（丁丙诺啡）相关的知识、态度和行为； 2) 识别并描述网络论坛上反映的非法使用这些药物的时间模式。为了收集数据，该研究将使用允许自由讨论非法药物、包含非法处方药使用信息并可供公众查看的网站。该研究将产生有关丁丙诺啡滥用行为的新信息，并将通过提供处理快速增长的网络数据所需的自动编码和信息提取工具，为公共卫生和药物滥用研究的进步做出贡献。本研究中应用的自动信息提取方法将增强当前的预警和流行病学监测系统，并可以推进公共卫生其他领域的定性和基于网络的研究方法。

项目成果

"I just wanted to tell you that loperamide WILL WORK": a web-based study of extra-medical use of loperamide.

“我只是想告诉你洛哌丁胺会起作用”：一项关于洛哌丁胺的非医疗用途的网络研究。

• 发表时间: 2013-06-01
• 影响因子: 4.2
• 作者: Daniulaityte, Raminta;Carlson, Robert;Falck, Russel;Cameron, Delroy;Perera, Sujan;Chen, Lu;Sheth, Amit
• 通讯作者: Sheth, Amit

PREDOSE: a semantic web platform for drug abuse epidemiology using social media.

PREDOSE：使用社交媒体进行药物滥用流行病学语义网络平台。

• 发表时间: 2013-12
• 影响因子: 4.5
• 作者: Cameron, Delroy;Smith, Gary A;Daniulaityte, Raminta;Sheth, Amit P;Dave, Drashti;Chen, Lu;Anand, Gaurish;Carlson, Robert;Watkins, Kera Z;Falck, Russel
• 通讯作者: Falck, Russel

A graph-based recovery and decomposition of Swanson's hypothesis using semantic predications.

使用语义预测对斯旺森假设进行基于图的恢复和分解。

• 发表时间: 2013-04
• 影响因子: 4.5
• 作者: Cameron, Delroy;Bodenreider, Olivier;Yalamanchili, Hima;Danh, Tu;Vallabhaneni, Sreeram;Thirunarayan, Krishnaprasad;Sheth, Amit P;Rindflesch, Thomas C
• 通讯作者: Rindflesch, Thomas C

"Sub is a weird drug:" A web-based study of lay attitudes about use of buprenorphine to self-treat opioid withdrawal symptoms.

“Sub 是一种奇怪的药物：”一项基于网络的研究，调查了外行人对使用丁丙诺啡自我治疗阿片类药物戒断症状的态度。

• 发表时间: 2015-08
• 作者: Daniulaityte, Raminta;Carlson, Robert;Brigham, Gregory;Cameron, Delroy;Sheth, Amit
• 通讯作者: Sheth, Amit