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

BACKGROUND Abrupt cessation of opioid use can cause withdrawal symptoms, ranging from moderate to severe. People often continue opioid misuse to avoid these symptoms. Many people who use opioids self-treat withdrawal symptoms with a wide range of substances, some of which could help and some potentially harm. Little is known about the substances people use or their effects. OBJECTIVE 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. METHODS We developed a named entity recognition model and used it to extract substances and effects from nearly 4 million comments from the r/opiates and r/OpiatesRecovery subreddits. We categorized effects as (1) DSM-5 symptoms of opioid withdrawal, e.g., body aches, (2) effects of opioid use, e.g., euphoria, (3) neither, or (4) other. In this analysis, we focused on those effects which are symptoms of opioid withdrawal and substances which are potential remedies for those withdrawal symptoms. To identify these subsets, we began by deduplicating substances and effects using a combination of clustering on word embeddings 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 top 10 substances most strongly associated with the effect, based on a weighted average of edge count and positive pointwise mutual information. We classified these symptom and potential remedy pairs as (1) common treatments, (2) not accepted practice but potentially useful, (3) natural/home remedies, (4) causes, or (5) other. We developed the Withdrawal Remedy Explorer app to facilitate further exploration of the data. RESULTS Our named entity recognition 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 common, accepted treatments for the symptom; 13.08% were not accepted practice, but could be useful given their pharmacology; 10.00% were natural/home remedies; 5.38% were causes of the symptom; and 30.00% were other. We identified both potentially promising new remedies (e.g., gabapentin for body aches) and potentially common but harmful remedies (e.g., antihistamines for restless leg syndrome). CONCLUSIONS 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 this 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 (to any degree) and discuss their experiences online.