Predicting changes in substance use following psychedelic experiences: natural language processing of psychedelic session narratives.

The American Journal of Drug and Alcohol Abuse
David J CoxMatthew W Johnson

Abstract

Background: Experiences with psychedelic drugs, such as psilocybin or lysergic acid diethylamide (LSD), are sometimes followed by changes in patterns of tobacco, opioid, and alcohol consumption. But, the specific characteristics of psychedelic experiences that lead to changes in drug consumption are unknown.Objective: Determine whether quantitative descriptions of psychedelic experiences derived using Natural Language Processing (NLP) would allow us to predict who would quit or reduce using drugs following a psychedelic experience.Methods: We recruited 1141 individuals (247 female, 894 male) from online social media platforms who reported quitting or reducing using alcohol, cannabis, opioids, or stimulants following a psychedelic experience to provide a verbal narrative of the psychedelic experience they attributed as leading to their reduction in drug use. We used NLP to derive topic models that quantitatively described each participant's psychedelic experience narrative. We then used the vector descriptions of each participant's psychedelic experience narrative as input into three different supervised machine learning algorithms to predict long-term drug reduction outcomes.Results: We found that the topic models derived throu...Continue Reading

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