Using Named Entity Recognition to Identify Substances Used in Self-Medication of Opioid Withdrawal: Natural Language Processing Study of Reddit Data (Preprint)

Abstract

Cessation of opioid use can cause withdrawal symptoms. People often continue opioid misuse to avoid these symptoms. Many people who use opioids self-treat withdrawal symptoms with a range of substances. Little is known about the substances people use or their effects.

To validate a methodology for identifying substances used to treat symptoms of opioid withdrawal by a community of people who use opioids on the social media site Reddit.

We developed a named entity recognition (NER) model and used it to extract substances and effects from nearly 4 million comments from the r/opiates and r/OpiatesRecovery subreddits. We focused on effects that are symptoms of opioid withdrawal and substances that are potential remedies for those symptoms. To identify these groups, we began by deduplicating substances and effects using a combination of clustering and manual review. We then built a bipartite network of substance and effect co-occurrence. For each of 16 effects identified as DSM-5 symptoms of opioid withdrawal, we identified the 10 most strongly associated substances, based on a weighted average of edge count and positive pointwise mutual information. We classified these symptom and potential remedy pairs as (1) substance is an FDA-approved or commonly utilized treatment for symptom, (2) substance is not often used to treat symptom but could be potentially useful given its pharmacological profile, (3) substance is a home/natural remedy for symptom, (4) substance can cause symptom, or (5) other/unclear. We developed the Withdrawal Remedy Explorer app to facilitate further exploration of the data.

Our NER model achieved F1 scores of 92.1 (substances) and 81.7 (effects) on holdout data. After deduplication, we identified 458 unique substances and 253 unique effects. Of 130 potential remedies strongly associated with withdrawal symptoms, 41.54% were FDA-approved or commonly utilized treatments for the symptom; 13.08% were not often used to treat the symptom but could be potentially useful given their pharmacological profile; 10.00% were natural/home remedies; 5.38% were causes of the symptom; and 30.00% were other/unclear. We identified both potentially promising remedies (e.g., gabapentin for body aches) and potentially common but harmful remedies (e.g., antihistamines for restless leg syndrome).

Social media is a promising source of data on self-medication of opioid withdrawal. Many of the withdrawal remedies discussed by Reddit users are either clinically proven or potentially useful. These results suggest that this methodology is a valid way to study the self-treatment behavior of an online community of people who use opioids. Our Withdrawal Remedy Explorer app provides a platform to use these data for pharmacovigilance, identification of new treatments, and better understanding the needs of people undergoing opioid withdrawal. Furthermore, this approach could be applied to many other disease states where people self-manage their symptoms and discuss their experiences online.