Machine Learning to Detect Illegal Online Sales of Prescription Opioids

1. Detecting Illicit Sales of
Opioids using AI
Janani Kalyanam
Senior Data Scientist
Intuit AI

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. 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. 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. 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. Data Collection
codeine
fentanyl
hydrocodone
oxycodone
oxycontin
percocet
vicodin
Twitter Public
API
Close to 1M
tweets

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. Example “topics”

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. Overall Pipeline

11. Thank you!
Work done while a postdoc at UCSD - in collaboration with Timothy Mackey, Takeo
Katsuki and Gert Lanckriet.