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Janani Kalyanam - Machine Learning to Detect Illegal Online Sales of Prescription Opioids


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Machine Learning to Detect Illegal Online Sales of Prescription Opioids

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Janani Kalyanam - Machine Learning to Detect Illegal Online Sales of Prescription Opioids

  1. 1. Detecting Illicit Sales of Opioids using AI Janani Kalyanam Senior Data Scientist Intuit AI
  2. 2. Ryan Haight Online Pharmacy Consumer Protection Act, 2008 ● Ryan (teenager) dies of drug overdose. He had purchased vicodin online from a “no prescription pharmacy”. ● Specifically address the effect of internet to illegally market and sell controlled substances.
  3. 3. Policy Based Intervention is not Effective ● Establish guidelines as a way of regulation. ● However the role of internet and its effect is still not adequately addressed. ● NAPB reviewed 110,000 websites, and found 96% were not in compliance. Example - they did not require valid prescriptions, or issued prescriptions using questionnaires only.
  4. 4. Drug Abuse and Social Media ● clear association between drug abuse and social media ● content about both behavior and access Our Answer Necessitates building AI-powered models to automatically, quickly analyze social media content and detect the emergence of bad actors as quickly as possible.
  5. 5. Problem Set Up Magic AI List of “bad” websites > 350K messages published every minute Since these are public platforms, there is a huge variety in the content. After we find the list of websites - what next?
  6. 6. Data Collection codeine fentanyl hydrocodone oxycodone oxycontin percocet vicodin Twitter Public API Close to 1M tweets
  7. 7. Modeling the Data ● Main source of noise is news ● Need to separate out the tweets that are related to illicit online pharmacies. Close to 1M tweets Biterm Topic Model Isolate topics that are relevant to Illicit Online Pharmacies
  8. 8. Example “topics”
  9. 9. Characteristics of Isolated Tweets ● User Engagement Features ○ favorite_count, in_reply_to_status_id, retweet_count, retweeted_status ● Content Based Features ○ entities_urls, entities_hashtags, entities_symbols, possibly_sensitive ● Network Based Features ○ user_followers_count, user_friends_count ● User Profile Features ○ user_statuses_count, user_favorites_count, user_verified
  10. 10. Overall Pipeline
  11. 11. Thank you! Work done while a postdoc at UCSD - in collaboration with Timothy Mackey, Takeo Katsuki and Gert Lanckriet.