Behavioral Pharmacology Research Unit, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Department of Data & Analytics, Behavioral Health Center of Excellence, Los Angeles, California, USA.
Am J Drug Alcohol Abuse. 2021 Jul 4;47(4):444-454. doi: 10.1080/00952990.2021.1910830. Epub 2021 Jun 5.
: 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.: 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.: 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.: 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.: 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.
体验迷幻药物，如裸盖菇素或麦角酸二乙酰胺（LSD），有时会导致烟草、阿片类药物和酒精消费模式的改变。但是，导致药物消费变化的迷幻体验的具体特征尚不清楚。
确定使用自然语言处理（NLP）得出的迷幻体验的定量描述是否能够预测在迷幻体验后谁会停止或减少使用毒品。
我们从在线社交媒体平台招募了 1141 名（247 名女性，894 名男性）参与者，他们报告在迷幻体验后停止或减少使用酒精、大麻、阿片类药物或兴奋剂，并提供导致他们减少药物使用的迷幻体验的口头叙述。我们使用 NLP 得出主题模型，定量描述每个参与者的迷幻体验叙述。然后，我们将每个参与者的迷幻体验叙述的向量描述作为输入，输入到三种不同的监督机器学习算法中，以预测长期药物减少的结果。
我们发现，通过 NLP 得出的主题模型导致了参与者叙述的定量描述，这些描述在按药物类别停止以及长期停止/减少结果分组时，参与者之间存在差异。此外，所有三种机器学习算法都导致了类似的长期停止/减少结果的预测准确性（约 65%，CI=±0.21%）。
使用机器学习分析迷幻体验的书面报告可能能够准确预测停药结果以及在迷幻治疗中停止或减少哪种药物。
