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）：药物阿片类药物的非医学使用已被确定为美国增长最快的药物滥用形式之一，迫切需要增强当前的流行病学监测，预警和市场后的监测通过提供其他更及时的数据来系统。万维网已被确定为检测药物使用实践模式和变化的“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。此类用户生成的内容（UGC）可以用作非常丰富的数据源，用于研究与非法药物有关的知识，态度和行为。为了利用网络对药物滥用研究的全部潜力，该领域需要开发一种高度自动化的方式来访问，提取和分析与非法药物使用有关的基于Web的数据。该探索性R21是一名多项式研究者，是干预，治疗和成瘾研究中心研究人员（Citar）的研究人员与赖特州立大学知识知识信息服务与科学中心（KNO.E.SIS）之间的合作努力。这项基于网络的研究的目的是应用最先进的信息处理技术，例如语义网络，自然语言处理和机器学习，对用户生成的内容的定性和定量内容分析来实现以下目的：1）描述吸毒者的知识，态度和行为与非法使用亚毒素（R）（丁丙诺啡/纳洛酮）和Subutex（R）（丁丙诺啡）有关； 2）识别并描述了基于Web的论坛上反映的这些药物的非法使用的时间模式。为了收集数据，该研究将使用允许对非法药物进行免费讨论的网站，其中包含有关非法处方药使用的信息，并且可以访问公众观看。这项研究将生成有关丁丙诺啡滥用实践的新信息，并通过提供处理快速增长的基于Web的数据所需的自动编码和信息提取工具，从而有助于公共卫生和药物滥用研究。本研究中采用的自动信息提取方法将增强当前的预警和流行病学监视系统，并可以推进其他公共卫生其他领域的定性和基于Web的研究方法。

项目成果

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

• DOI: 10.1111/ajad.12213
• 发表时间: 2015-08
• 期刊: The American journal on addictions
• 作者: Daniulaityte R;Carlson R;Brigham G;Cameron D;Sheth A
• 通讯作者: Sheth A

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

• DOI: 10.1016/j.jbi.2012.09.004
• 发表时间: 2013-04
• 期刊: JOURNAL OF BIOMEDICAL INFORMATICS
• 影响因子: 4.5
• 作者: Cameron, Delroy;Bodenreider, Olivier;Yalamanchili, Hima;Tu Danh;Vallabhaneni, Sreeram;Thirunarayan, Krishnaprasad;Sheth, Amit P.;Rindflesch, Thomas C.
• 通讯作者: Rindflesch, Thomas C.

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

• DOI: 10.1016/j.jbi.2013.07.007
• 发表时间: 2013-12
• 期刊: JOURNAL OF BIOMEDICAL INFORMATICS
• 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