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Tim K Mackey - One of the best experts on this subject based on the ideXlab platform.
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Online Marketing practices of regenerative medicine clinics in us mexico border region a web surveillance study
Stem Cell Research & Therapy, 2021Co-Authors: Javier Chavez, Neal Shah, S Ruoss, Raphael E Cuomo, Tim K MackeyAbstract:INTRODUCTION The potential of regenerative medicine to improve human health has led to the rapid expansion of stem cell clinics throughout the world with varying levels of regulation and oversight. This has led to a market ripe for stem cell tourism, with Tijuana, Mexico, as a major destination. In this study, we characterize the Online Marketing, intervention details, pricing of services, and assess potential safety risks through web surveillance of regenerative medicine clinics Marketing services in Tijuana. METHODS We conducted structured Online search queries from March to April 2019 using 296 search terms in English and Spanish on two search engines (Google and Bing) to identify websites engaged in direct-to-consumer advertising of regenerative medicine services. We performed content analysis to characterize three categories of interest: Online presence, tokens of scientific legitimacy, and intervention details. RESULTS Our structured Online searches resulted in 110 unique websites located in Tijuana corresponding to 76 confirmed locations. These clinics' Online presence consisted of direct-to-consumer advertising mainly through a dedicated website (94.5%) or Facebook page (65.5%). The vast majority of these websites (99.1%) did not mention any affiliation to an academic institutions or other overt tokens of scientific legitimacy. Most clinics claimed autologous tissue was the source of treatments (67.3%) and generally did not specify route of administration. Additionally, of the Tijuana clinics identified, 13 claimed licensing, though only 1 matched with available licensing information. CONCLUSIONS Regenerative medicine clinics in Tijuana have a significant Online presence using direct-to-consumer advertising to attract stem-cell tourism clientele in a bustling border region between Mexico and the USA. This study adds to existing literature evidencing the unregulated nature of Online stem cell offerings and provides further evidence of the need for regulatory harmonization, particularly to address stem cell services being offered Online across borders.
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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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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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illicit Online Marketing of lorcaserin before dea scheduling
Obesity, 2013Co-Authors: Bryan A Liang, Tim K Mackey, Ashley N Archerhayes, Linda M ShinnAbstract:Objective: Antiobesity drugs have been marketed illicitly by “no prescription” Online pharmacies after approval and scheduling by the drug enforcement agency. We assess whether antiobesity drug Belviq® (lorcaserin HCl) was available from illicit Online vendors before DEA-scheduling when sales are unauthorized. Design and Methods: Online searches of “buy Belviq no prescription” examining first five result pages Marketing the drug. Searches were performed from 11/5/2012-12/8/2012, prior to DEA scheduing. Results: Belviq® is actively marketed by “no prescription” Online vendors despite official unavailability and prescription requirements. Approaches included direct-to-consumer advertising using descriptive website URLs; linking to illicit marketers; and directing customers to other weight-loss websites for additional Marketing. Finally, large quantities were marketed by business-to-business vendors. Conclusion: Illicit Online “no prescription” pharmacies are Marketing unauthorized, suspect antiobesity drugs before DEA scheduling and permitted Marketing. Regulators must legally intercede to ensure patient safety, and providers must educate patients about Online-sourcing risks.
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.
Călin Gurău - One of the best experts on this subject based on the ideXlab platform.
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Integrated Online Marketing communication: implementation and management
Journal of Communication Management, 2008Co-Authors: Călin GurăuAbstract:Purpose – The purpose of this paper is to investigate the particularities of integrated Marketing communication (IMC) in the Online environment.Design/methodology/approach – Both secondary and primary data (face‐to‐face interviews with 29 Marketing or communication managers of UK Online consumer retail firms) are analysed in order to identify the various meanings of the integrated Online Marketing communication, the opportunities and challenges raised by Online communication, and the structure of an efficient integrated Online Marketing communication system.Findings – The transparency, interactivity and memory of the internet force the organisation to adopt a proactive‐reactive attitude in Online communication, and to combine consistency and continuity with flexibility and customisation.Research limitations/implications – The number of interviews used to collect primary data is relatively small; the use of the information collected is general and unstructured; and the findings are applicable only to onlin...
Josh Klugman - 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.
Janani Kalyanam - 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.
