The Experts below are selected from a list of 6474 Experts worldwide ranked by ideXlab platform
Tim K Mackey - One of the best experts on this subject based on the ideXlab platform.
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solution to detect classify and report illicit Online marketing and sales of controlled substances via twitter using machine learning and web forensics to combat digital opioid access
Journal of Medical Internet Research, 2018Co-Authors: Tim K Mackey, Janani Kalyanam, Josh Klugman, Ella Kuzmenko, Rashmi GuptaAbstract:Background: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. Objective: The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit Online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Methods: Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal Online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal Online sellers. Results: We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal Online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit Online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Conclusions: The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit Online Pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the Online environment safer for the public. [J Med Internet Res 2018;20(4):e10029]
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solution to detect classify and report illicit Online marketing and sales of controlled substances via twitter using machine learning and web forensics to combat digital opioid access
Journal of Medical Internet Research, 2018Co-Authors: Tim K Mackey, Janani Kalyanam, Josh Klugman, Ella Kuzmenko, Rashmi GuptaAbstract:Background: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. Objective: The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit Online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Methods: Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal Online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal Online sellers. Results: We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal Online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit Online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Conclusions: The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit Online Pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the Online environment safer for the public.
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digital danger a review of the global public health patient safety and cybersecurity threats posed by illicit Online pharmacies
British Medical Bulletin, 2016Co-Authors: Tim K Mackey, Gaurvika NayyarAbstract:Amidst the rise of e-commerce, there has been a proliferation of illicit Online pharmacies that threaten global patient safety by selling drugs without a prescription directly to the consumer. Despite this clear threat, little is known about the key risk characteristics, central challenges and current legal, regulatory and law enforcement responses.A review was conducted of the English literature with search terms 'Online pharmacies', 'Internet pharmacies', 'cyber pharmacies', 'rogue pharmacies', and 'e-pharmacies' using PubMed, JSTOR, and Google Scholar from 1999-2005.Illicit Online pharmacies are a rapidly growing public health threat and are characterized by a number of complex and interrelated risk factors.Solutions are varied and are of questionable utility in the face of evolving technology that enables this form of transnational cybercrime.Legal, regulatory and technology solutions must address the entire illicit Online Pharmacy ecosystem in order to be effective.There is a critical need to build international consensus, conduct additional research and develop technology to combat illicit Online pharmacies.
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digital danger a review of the global public health patient safety and cybersecurity threats posed by illicit Online pharmacies
British Medical Bulletin, 2016Co-Authors: Tim K Mackey, Gaurvika NayyarAbstract:BACKGROUND Amidst the rise of e-commerce, there has been a proliferation of illicit Online pharmacies that threaten global patient safety by selling drugs without a prescription directly to the consumer. Despite this clear threat, little is known about the key risk characteristics, central challenges and current legal, regulatory and law enforcement responses. SOURCES OF DATA A review was conducted of the English literature with search terms 'Online pharmacies', 'Internet pharmacies', 'cyber pharmacies', 'rogue pharmacies', and 'e-pharmacies' using PubMed, JSTOR, and Google Scholar from 1999-2005. AREAS OF AGREEMENT Illicit Online pharmacies are a rapidly growing public health threat and are characterized by a number of complex and interrelated risk factors. AREAS OF CONTROVERSY Solutions are varied and are of questionable utility in the face of evolving technology that enables this form of transnational cybercrime. GROWING POINTS Legal, regulatory and technology solutions must address the entire illicit Online Pharmacy ecosystem in order to be effective. AREAS TIMELY FOR DEVELOPING RESEARCH There is a critical need to build international consensus, conduct additional research and develop technology to combat illicit Online pharmacies.
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establishing a link between prescription drug abuse and illicit Online pharmacies analysis of twitter data
Journal of Medical Internet Research, 2015Co-Authors: Takeo Katsuki, Tim K Mackey, Raphael E CuomoAbstract:Background: Youth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens. Objective: In order to better assess NUPM behavior Online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via Online pharmacies. Methods: Tweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal Online access to prescription drugs using a protocol of content coding and supervised machine learning. Results: A total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72% of the generic and 12.28% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72% of the tweets with URLs included a hyperlink to an Online marketing affiliate that directly linked to an illicit Online Pharmacy advertising the sale of Valium without a prescription. Conclusions: This study examined the association between Twitter content, NUPM behavior promotion, and Online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal Online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, Online health promotion activities, and public policy to combat NUPM Online. [J Med Internet Res 2015;17(12):e280]
Gaurvika Nayyar - One of the best experts on this subject based on the ideXlab platform.
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digital danger a review of the global public health patient safety and cybersecurity threats posed by illicit Online pharmacies
British Medical Bulletin, 2016Co-Authors: Tim K Mackey, Gaurvika NayyarAbstract:Amidst the rise of e-commerce, there has been a proliferation of illicit Online pharmacies that threaten global patient safety by selling drugs without a prescription directly to the consumer. Despite this clear threat, little is known about the key risk characteristics, central challenges and current legal, regulatory and law enforcement responses.A review was conducted of the English literature with search terms 'Online pharmacies', 'Internet pharmacies', 'cyber pharmacies', 'rogue pharmacies', and 'e-pharmacies' using PubMed, JSTOR, and Google Scholar from 1999-2005.Illicit Online pharmacies are a rapidly growing public health threat and are characterized by a number of complex and interrelated risk factors.Solutions are varied and are of questionable utility in the face of evolving technology that enables this form of transnational cybercrime.Legal, regulatory and technology solutions must address the entire illicit Online Pharmacy ecosystem in order to be effective.There is a critical need to build international consensus, conduct additional research and develop technology to combat illicit Online pharmacies.
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digital danger a review of the global public health patient safety and cybersecurity threats posed by illicit Online pharmacies
British Medical Bulletin, 2016Co-Authors: Tim K Mackey, Gaurvika NayyarAbstract:BACKGROUND Amidst the rise of e-commerce, there has been a proliferation of illicit Online pharmacies that threaten global patient safety by selling drugs without a prescription directly to the consumer. Despite this clear threat, little is known about the key risk characteristics, central challenges and current legal, regulatory and law enforcement responses. SOURCES OF DATA A review was conducted of the English literature with search terms 'Online pharmacies', 'Internet pharmacies', 'cyber pharmacies', 'rogue pharmacies', and 'e-pharmacies' using PubMed, JSTOR, and Google Scholar from 1999-2005. AREAS OF AGREEMENT Illicit Online pharmacies are a rapidly growing public health threat and are characterized by a number of complex and interrelated risk factors. AREAS OF CONTROVERSY Solutions are varied and are of questionable utility in the face of evolving technology that enables this form of transnational cybercrime. GROWING POINTS Legal, regulatory and technology solutions must address the entire illicit Online Pharmacy ecosystem in order to be effective. AREAS TIMELY FOR DEVELOPING RESEARCH There is a critical need to build international consensus, conduct additional research and develop technology to combat illicit Online pharmacies.
Rashmi Gupta - One of the best experts on this subject based on the ideXlab platform.
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solution to detect classify and report illicit Online marketing and sales of controlled substances via twitter using machine learning and web forensics to combat digital opioid access
Journal of Medical Internet Research, 2018Co-Authors: Tim K Mackey, Janani Kalyanam, Josh Klugman, Ella Kuzmenko, Rashmi GuptaAbstract:Background: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. Objective: The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit Online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Methods: Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal Online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal Online sellers. Results: We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal Online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit Online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Conclusions: The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit Online Pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the Online environment safer for the public. [J Med Internet Res 2018;20(4):e10029]
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solution to detect classify and report illicit Online marketing and sales of controlled substances via twitter using machine learning and web forensics to combat digital opioid access
Journal of Medical Internet Research, 2018Co-Authors: Tim K Mackey, Janani Kalyanam, Josh Klugman, Ella Kuzmenko, Rashmi GuptaAbstract:Background: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. Objective: The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit Online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Methods: Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal Online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal Online sellers. Results: We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal Online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit Online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Conclusions: The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit Online Pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the Online environment safer for the public.
Steven Paul Woods - One of the best experts on this subject based on the ideXlab platform.
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health literacy mediates the effects of educational attainment on Online Pharmacy navigation skills in older adults with hiv disease
Psychology & Health, 2021Co-Authors: Anastasia Matchanova, Erin E Morgan, Luis D Medina, David P Sheppard, Steven Paul WoodsAbstract:The Internet serves an increasingly critical role in health behaviors for older adults with chronic medical conditions. Guided by theories of health behaviors and literacy, this study examined whet...
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Online Pharmacy navigation skills are associated with prospective memory in hiv disease
Clinical Neuropsychologist, 2021Co-Authors: Anastasia Matchanova, Steven Paul Woods, Clint Cushman, Erin E Morgan, Luis D Medina, Michelle A Babicz, Marizela Verduzco, Shayne LoftAbstract:The increased use of Online Pharmacy services in the midst of the COVID-19 pandemic provides an important backdrop against which to examine the role of neurocognitive functions in health-related In...
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health related everyday functioning in the internet age hiv associated neurocognitive disorders disrupt Online Pharmacy and health chart navigation skills
Archives of Clinical Neuropsychology, 2016Co-Authors: Steven Paul Woods, Erin E Morgan, Jennifer E Iudicello, Marizela V Cameron, Katie L Doyle, Tyler V Smith, Clint CushmanAbstract:This study evaluated the effects of HIV-associated Neurocognitive Disorders (HAND) on 2 Internet-based tests of healthcare management. Study participants included 46 individuals with HIV infection, 19 of whom were diagnosed with HAND, and 21 seronegatives. Participants were administered Internet-based tests of Online Pharmacy and health records navigation skills in which they used mock credentials to log in to an experimenter-controlled website and independently perform a series of typical Online health-related behaviors (e.g., refill a prescription, read and interpret an electronic chart note). HAND was associated with significantly lower accuracy on both the Online Pharmacy and health records navigation tasks. Among the HIV+ participants, poorer performance on the Online healthcare navigation tasks was associated with fewer years of education, higher plasma viral load, less frequent Internet use, and lower health literacy. Findings indicate that individuals with HAND may have marked difficulties navigating the Internet to complete important health-related behaviors.
Ella Kuzmenko - One of the best experts on this subject based on the ideXlab platform.
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solution to detect classify and report illicit Online marketing and sales of controlled substances via twitter using machine learning and web forensics to combat digital opioid access
Journal of Medical Internet Research, 2018Co-Authors: Tim K Mackey, Janani Kalyanam, Josh Klugman, Ella Kuzmenko, Rashmi GuptaAbstract:Background: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. Objective: The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit Online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Methods: Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal Online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal Online sellers. Results: We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal Online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit Online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Conclusions: The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit Online Pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the Online environment safer for the public. [J Med Internet Res 2018;20(4):e10029]
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solution to detect classify and report illicit Online marketing and sales of controlled substances via twitter using machine learning and web forensics to combat digital opioid access
Journal of Medical Internet Research, 2018Co-Authors: Tim K Mackey, Janani Kalyanam, Josh Klugman, Ella Kuzmenko, Rashmi GuptaAbstract:Background: On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. Objective: The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit Online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Methods: Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal Online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal Online sellers. Results: We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal Online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit Online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Conclusions: The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit Online Pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the Online environment safer for the public.
