Instrument Wizard Works Magic With Web-Based Drug Abuse Research Tools

2004 ◽  
Keyword(s):  
2017 ◽  
Vol 16 (5) ◽  
pp. 249-273 ◽  
Author(s):  
Vaibhav Shukla ◽  
Vinay Koshy Varghese ◽  
Shama Prasada Kabekkodu ◽  
Sandeep Mallya ◽  
Kapaettu Satyamoorthy
Keyword(s):  

2020 ◽  
Author(s):  
Usha Lokala

BACKGROUND Web-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 utilization of these novel data sources for epidemiological surveillance of substance use behaviors and trends. OBJECTIVE The key aims are to describe development and application of the Drug Abuse Ontology as a framework for analyzing web-based data to inform public health surveillance in the following domains: 1) user knowledge, attitudes, and behaviors related to non-medical use of buprenorphine and other illicit opioids through analysis of web forum data; 2) patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the U.S through analysis of Twitter and web forum data; and 3) trends in the availability of novel synthetic opioids through analysis of crypto market data. METHODS The domain and scope of the drug abuse ontology were defined using competency questions from two popular ontology methodologies (Neon and 101 ontology development methodology). The quality of the ontology is evaluated with a set of tools and best practices recognized by the Semantic Web community and the AI community that engage in natural language processing. The standard ontology metrics are also presented. The ontology was manually developed by the domain experts from the Center for Interventions, Treatment, and Addictions Research (CITAR) who used a range of data sources: 1) key epidemiological data sources and reports accessible through National Institute on Drug Abuse, Drug Enforcement Agency, European Monitoring Centre for Drugs Addiction, RxNorm and other; 2) prior peer-reviewed publications related to illicit opioids, cannabis, and other drugs; and 3) preliminary assessment and examination of web-based, social media sources related to selected substances. Sources of types 1 and 2 provided primary concepts while sources of type 3 were important in identifying alternative concepts including synonyms and street names. RESULTS The current version of Drug Abuse Ontology 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 decreases the false alarm rate by adding external knowledge to the learning process. The ontology is being updated to capture evolving concepts and has been used for three different projects: PREDOSE, eDrugTrends, and eDarkTrends. CONCLUSIONS It is found that the developed DAO is useful to identify the most frequently used terms/slang terms on social media/dark web related to drug abuse posted by the general population on social media and vendors on the dark web.


2020 ◽  
Author(s):  
Usha Lokala ◽  
Raminta Daniulaityte ◽  
Francois Lamy ◽  
Manas Gaur ◽  
Krishnaprasad Thirunarayan ◽  
...  

BACKGROUND Web-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 utilization of these novel data sources for epidemiological surveillance of substance use behaviors and trends. OBJECTIVE The key aims are to describe the development and application of the Drug Abuse Ontology as a framework for analyzing web-based data to inform public health surveillance for the following applications: 1) determining user knowledge, attitudes, and behaviors related to non-medical use of buprenorphine and other illicit opioids through analysis of web forum data; 2) understanding patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the U.S through analysis of Twitter and web forum data; and 3) gleaning trends in the availability of novel synthetic opioids through analysis of crypto market data. METHODS The domain and scope of the drug abuse ontology were defined using competency questions from two popular ontology methodologies (Neon and 101 ontology development methodology). The quality of the ontology is evaluated with a set of tools and best practices recognized by the Semantic Web community and the AI community that engage in natural language processing. The standard ontology metrics are also presented. RESULTS The current version of Drug Abuse Ontology 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 decreases the false alarm rate by adding external knowledge to the learning process. The ontology is being updated to capture evolving concepts and has been used for four different projects: PREDOSE, eDrugTrends, eDarkTrends, DAO applications in Mental Health and COVID scenario. CONCLUSIONS It has been found that the developed Drug Abuse Ontology (DAO) is useful to identify the most frequently used terms/slang terms on social media/dark web related to drug abuse posted by the general population .


2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Farzad Jalilian ◽  
Mehdi Mirzaei Alavijeh ◽  
Mohammad Ahmadpanah ◽  
Behzad Karami Matin ◽  
Mari Ataee ◽  
...  

2020 ◽  
Author(s):  
Alexia Polillo ◽  
Aristotle N. Voineskos ◽  
George Foussias ◽  
Sean A. Kidd ◽  
Andreea Sav ◽  
...  

BACKGROUND Barriers to recruiting and retaining people with psychosis and their families in research are well-established, potentially biasing clinical research samples. Digital research tools, such as online platforms, mobile apps and text messaging, have the potential to address barriers to research by facilitating remote participation. However, there has been limited research on leveraging these technologies to engage people with psychosis and their families in research. OBJECTIVE The objective of this study was to assess the uptake of digital tools to engage patients with provisional psychosis and their families in research and their preferences for different research administration methods. METHODS This study used Research Electronic Data Capture (REDCap), a secure web-based platform with built-in tools for data collection and storage, to send web-based consent forms and surveys via text message or email to patients and families referred to early psychosis intervention services; potential participants were also approached or reminded about the study in person. We calculated completion rates and timing using remote and in-person methods and compensation preferences. RESULTS A total of 447 patients with provisional psychosis and 187 of their family members agreed to receive the web-based consent form, and approximately half of patients (48.3%; 216/447) and family members (58.3%; 109/187) consented to participate in the survey. Most patients (79.5%; 182/229) and family members (64.7%; 75/116) who completed the consent form did so remotely, with more family members (35.3%; 41/116) completing it in person than youth (20.5%; 47/229). Of those who consented, 77.3% (167/216) of patients and 72.5% (79/109) of family members completed the survey, and most did the survey remotely. Almost all patients (90.5%; 418/462) and family members (91.6%; 174/190) requested to receive the consent form and survey by email, and only 4.1% (19/462) and 3.2% (6/190) preferred text message. Just over half of patients (54.5%; 91/167) and family members (53.2%; 42/79) preferred to receive e-gift cards from a coffee shop as study compensation. Most surveys were completed during the week between 12 and 6 pm. CONCLUSIONS When offered the choice, most participants with psychosis and their families chose remote administration methods, suggesting that digital tools may enhance research recruitment and participation in this population, particularly in the context of the COVID-19 global pandemic.


2019 ◽  
Vol 93 ◽  
pp. 86-92 ◽  
Author(s):  
Traci Marie Schwinn ◽  
Steven Paul Schinke ◽  
Bryan Keller ◽  
Jessica Hopkins

1998 ◽  
Vol 62 (9) ◽  
pp. 671-674
Author(s):  
JF Chaves ◽  
JA Chaves ◽  
MS Lantz
Keyword(s):  

2013 ◽  
Vol 23 (3) ◽  
pp. 82-87 ◽  
Author(s):  
Eva van Leer

Mobile tools are increasingly available to help individuals monitor their progress toward health behavior goals. Commonly known commercial products for health and fitness self-monitoring include wearable devices such as the Fitbit© and Nike + Pedometer© that work independently or in conjunction with mobile platforms (e.g., smartphones, media players) as well as web-based interfaces. These tools track and graph exercise behavior, provide motivational messages, offer health-related information, and allow users to share their accomplishments via social media. Approximately 2 million software programs or “apps” have been designed for mobile platforms (Pure Oxygen Mobile, 2013), many of which are health-related. The development of mobile health devices and applications is advancing so quickly that the Food and Drug Administration issued a Guidance statement with the purpose of defining mobile medical applications and describing a tailored approach to their regulation.


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