Online Evidence of Withdrawal Self-Medication
戒断自我药物治疗的在线证据
基本信息
- 批准号:9979829
- 负责人:
- 金额:$ 26.82万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-08-01 至 2022-07-31
- 项目状态:已结题
- 来源:
- 关键词:Acupuncture TherapyAdultArtificial IntelligenceAutomationBeliefBenchmarkingCategoriesCluster AnalysisCollaborationsCommunitiesDataData SetDatabasesDrug PrescriptionsEffectivenessElementsEpidemiologistEpidemiologyFoodFood AdditivesFood SupplementsFutureHabitsHarm ReductionHerbHerbal MedicineHumanInformation RetrievalKnowledgeLanguageLifeLinkMarijuanaMedicalMeditationMethodologyMethodsModelingNatural Language ProcessingNon-Prescription DrugsOpioidPathway AnalysisPatient Self-ReportPatientsPharmaceutical PreparationsPhysiciansPilot ProjectsProcessPublished CommentRelapseReportingResearchResourcesSelf MedicationSupervisionTerminologyTherapeutic EffectTraditional MedicineTwitterUnited States Food and Drug AdministrationVariantVitaminsWithdrawalWithdrawal SymptomYogaalternative treatmentcostcravingdietary supplementsepidemiology studyexperienceexperimental studyinterestnon-opioid analgesicnoveloff-label drugoff-label useonline communityopioid misuseopioid useopioid useropioid withdrawalpatient populationself helpsocial mediaspellingtooltrend
项目摘要
PROJECT SUMMARY/ABSTRACT
Withdrawal symptoms from opioid use can be severe and are major contributing factors to relapse and
continuing misuse. Many opioid users are actively experimenting with “remedies” that can alleviate
withdrawal, and they are discussing their effectiveness in blogs and forums. In this pilot study we will use
Natural Language Processing (NLP) and human expertise to examine over 50,000 recent posts in two
Reddit forums OpiatesRecovery and Opiates to assess systematically which remedies are being used, how
they are being used, and what are the reported consequences of such self-help experimentation. We will
create a curated database of user-reported “remedies.” Information will be semiautomatically extracted from
the online, self-reported use of alternative treatments (i.e., other prescription drugs, over the counter
medications, food supplements, activities such as meditation and yoga). A team of a pharmacologist,
physician, and ethnographer will evaluate database entries to uncover (1) potential harm associated with
uncontrolled and unsupervised experimentation, (2) potentially effective available treatments (e.g., traditional
medicine), (3) potentially promising compound leads, and (4) patients' needs and issues that are most
important to them.
Aim 1. To assemble an extensive database of opioid withdrawal and remedy-associated terminology from
posts on OpiatesRecovery and Opiates Reddit communities. NLP will be used to build a language model that
understands how words are used in context (word2vec).
Aim 2. To develop a dataset of instances of self-reported remedy use from Reddit and conduct a bipartite
network analysis of remedies and users. Using NLP tools and the word embedding model we will develop an
exclusive dataset containing extracted information associated with remedies targeting withdrawal and
craving. This aim will use elements of artificial intelligence and close human supervision to extract remedies,
including variations of spelling, from the texts. The result of this aim will be a remedy database that includes
spelling variations and slang references and a network analysis linking remedies and users.
Aim 3. To organize, aggregate, and systematically assess information from mentions of remedy use.
Potential compounds and other remedies will be classified to provide an initial assessment of their potential
relevance to the opioid treatment process. This process will require the most human oversight and
assessment. Network analysis tools will be used to assess and identify the relationships between the types
of remedies and potential therapeutic effect and will create the benchmarks for similar future studies.
项目总结/文摘
项目成果
期刊论文数量(0)
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GEORGIY BOBASHEV的其他文献
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{{ truncateString('GEORGIY BOBASHEV', 18)}}的其他基金
Supplement for Cloud Computing: Opioid Policy Models
云计算的补充:阿片类药物政策模型
- 批准号:
10826888 - 财政年份:2020
- 资助金额:
$ 26.82万 - 项目类别:
Systems Approach to Modeling of Drug Use Recovery
药物使用回收建模的系统方法
- 批准号:
8224973 - 财政年份:2012
- 资助金额:
$ 26.82万 - 项目类别:
Systems Approach to Modeling of Drug Use Recovery
药物使用回收建模的系统方法
- 批准号:
8416409 - 财政年份:2012
- 资助金额:
$ 26.82万 - 项目类别:
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