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

This quantitative interview study (n=1141) applied a machine learning tool to analyze written reports of psychedelic experiences and predicted whether the participants could reduce substance abuse in response to using psychedelics with a 65% accuracy across three independently trained Natural Language Processing models.

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.”

Authors: David J. Cox, Albert Garcia-Romeu & Matthew W. Johnson

Notes

The participants in the study were reducing or quitting “alcohol n = 512; cannabis n = 272; opioids n = 195; stimulants n = 162.

The model was trained on 75% of the reports, and then predicted the outcomes for 25% of the remaining reports.

Imagine a computer researcher who’s presented with a page of text and the task of dissecting if the text represents a positive or negative mood. The researcher could read the paper and ‘process’ it to come to a conclusion one way or another. What if we change the scenario and now ask the researcher to do the same for 1000 pages of text? Then we can expect the researcher to pop open a can of Red Bull and start typing away making a ‘Natural Language Processing’ (NLP).

NLPs can analyze text and draw conclusions or insights from a text. This has been used to gauge the mood of voters based on their social media posts, the interest in stocks, and now also the content of someone’s psychedelic trip. NLPs are a part of machine learning or even more broadly artificial intelligence. Using this technique, psychedelic researchers have been able to analyze more than 1100 trip reports to understand what makes people quit drugs (alcohol, cannabis, opioids).

How did they do it?

  • The NLP algorithm analyzed the trip reports that participants wrote. This trip was the one that prompted them to quit taking a drug
  • Based on the text of the report, through trial and error with a subset of the data, the algorithm was able to predict if someone had actually quit a drug with about 65% accuracy
  • This type of approach may be useful for future studies where the written trip report could help identify the people who will quit a drug and those who might need more guidance

Still, this type of analysis is in its infancy. Clinicians may be able to use this type of data as another data point if they are treating a patient for substance addiction, but at this time it does nothing more than giving a likelihood of success. Finding out who’s less likely to achieve a reduction or quitting a drug could be beneficial in putting more resources towards integration and support.

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