David J Cox1,2, Albert Garcia-Romeu1, Matthew W Johnson1. 1. Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 2. Department of Data & Analytics, Behavioral Health Center of Excellence, Los Angeles, California, USA.
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 through NLP led to quantitative descriptions of participant narratives that differed across participants when grouped by the drug class quit as well as the long-term quit/reduction outcomes. Additionally, all three machine learning algorithms led to similar prediction accuracy (~65%, CI = ±0.21%) for long-term quit/reduction outcomes.Conclusions: Using machine learning to analyze written reports of psychedelic experiences may allow for accurate prediction of quit outcomes and what drug is quit or reduced within psychedelic therapy.
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 through NLP led to quantitative descriptions of participant narratives that differed across participants when grouped by the drug class quit as well as the long-term quit/reduction outcomes. Additionally, all three machine learning algorithms led to similar prediction accuracy (~65%, CI = ±0.21%) for long-term quit/reduction outcomes.Conclusions: Using machine learning to analyze written reports of psychedelic experiences may allow for accurate prediction of quit outcomes and what drug is quit or reduced within psychedelic therapy.
Entities:
Keywords:
Psychedelic treatment; hallucinogens; natural language processing; verbal behavior
Authors: John R Kelly; Claire M Gillan; Jack Prenderville; Clare Kelly; Andrew Harkin; Gerard Clarke; Veronica O'Keane Journal: Front Psychiatry Date: 2021-12-17 Impact factor: 4.157