ArticleJMIR public health and surveillance2022
Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study.
Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Interpol review of forensic drug chemistry, 2022-2025.Forensic science international. Synergy · 2026Review
- A knowledge-based clinical decision support system for personalized health examination items in China: design and evaluation.BMC medical informatics and decision making · 2025Article
- Knowledge graph and its application in the study of neurological and mental disorders.Frontiers in psychiatry · 2025Review
- Discovering opioid slang on social media: A Word2Vec approach with reddit data.Drug and alcohol dependence reports · 2024Article
- The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.JMIR infodemiology · 2024Article
- Detecting Substance Use Disorder Using Social Media Data and the Dark Web: Time- and Knowledge-Aware Study.JMIRx med · 2024Article
- Refining Substance Use Classification: An Ontological Framework for Enhancing Large-Scale Data Collection.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study.JMIR formative research · 2023Article
- Knowledge Representation and Management 2022: Findings in Ontology Development and Applications.Yearbook of medical informatics · 2023Review
- Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS.PloS one · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundWeb-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends.
objectiveThe key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance; analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends); assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America reports.
methodsThe domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The 101 method includes determining the domain and scope of ontology, reusing existing knowledge, enumerating important terms in ontology, defining the classes, their properties and creating instances of the classes. The quality of the ontology was evaluated using a set of tools and best practices recognized by the semantic web community and the artificial intelligence community that engage in natural language processing.
resultsThe current version of the DAO comprises 315 classes, 31 relationships, and 814 instances among the classes. The ontology is flexible and can easily accommodate new concepts. The integration of the ontology with machine learning algorithms dramatically decreased the false alarm rate by adding external knowledge to the machine learning process. The ontology is recurrently updated to capture evolving concepts in different contexts and applied to analyze data related to social media and dark web marketplaces.
conclusionsThe DAO provides a powerful framework and a useful resource that can be expanded and adapted to a wide range of substance use and mental health domains to help advance big data analytics of web-based data for substance use epidemiology research.
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