Predicting changes in substance use following psychedelic experiences: natural language processing of psychedelic session narratives
David J. Cox, Albert Garcia‐romeu, Matthew W. Johnson
The American Journal of Drug and Alcohol Abuse June 5, 2021 DOI: 10.1080/00952990.2021.1910830 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 1,141 |
| Population | Individuals from online social media platforms who reported quitting or reducing drug use after a psychedelic experience |
| Topics | Cannabis |
| Keywords | Narrative Harm reduction Clinical psychology Artificial intelligence |
| Citations | 29 |
| Key findings | Natural language processing of psychedelic experience narratives enabled machine learning models to predict long-term drug reduction outcomes with about 65% accuracy. |
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.
Comparable studies
Other observational and cohort studies on cannabis, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| High-potency cannabis and the risk of psychosis People with first episode of psychosis and healthy controls from the local population | 2009 | Case-control study | n = 454 |
| Ayahuasca-Assisted Therapy for Addiction: Results from a Preliminary Observational Study in Canada Rural aboriginal population | 2013 | Observational study | |
| Impaired cognitive performance in drug free users of recreational ecstasy (MDMA) Otherwise healthy young people | 2000 | Observational | |
| Continued cannabis use and risk of incidence and persistence of psychotic symptoms: 10 year follow-up cohort study Individuals from the general population in Germany aged 14-24 at baseline | 2011 | Observational cohort | n = 1,923 |
| Cannabis-induced psychosis and subsequent schizophrenia-spectrum disorders: follow-up study of 535 incident cases Patients treated for cannabis-induced psychotic symptoms in Denmark between 1994 and... | 2005 | Observational cohort | n = 535 |
Citations in the library
Cited by 12
- Psychedelic Therapy's Transdiagnostic Effects: A Research Domain Criteria (RDoC) Perspective Frontiers in Psychiatry December 17, 2021
- Classic psychedelics and alcohol use disorders: A systematic review of human and animal studies Addiction Biology August 31, 2022
- Journeying to Ixtlan: Ethics of Psychedelic Medicine and Research for Alzheimer's Disease and Related Dementias. AJOB Neuroscience 2023
- Analysis of recreational psychedelic substance use experiences classified by substance Psychopharmacology January 15, 2022
- Language as a Window Into the Altered State of Consciousness Elicited by Psychedelic Drugs Frontiers in Pharmacology March 22, 2022
and 7 more in the library
Cites 19
- Psilocybin produces substantial and sustained decreases in depression and anxiety in patients with life-threatening cancer: A randomized double-blind trial Journal of Psychopharmacology November 30, 2016
- Human hallucinogen research: guidelines for safety. Journal of Psychopharmacology July 1, 2008
- Psilocybin-assisted treatment for alcohol dependence: A proof-of-concept study Journal of Psychopharmacology January 13, 2015
- Pilot study of the 5-HT2AR agonist psilocybin in the treatment of tobacco addiction Journal of Psychopharmacology September 11, 2014
- Long-term follow-up of psilocybin-facilitated smoking cessation The American Journal of Drug and Alcohol Abuse July 21, 2016
and 14 more in the library