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Applying machine learning to international drug monitoring: classifying cannabis resin collected in Europe using cannabinoid concentrations.

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Title: Applying machine learning to international drug monitoring: classifying cannabis resin collected in Europe using cannabinoid concentrations.
Authors: Freeman, Tom P. (AUTHOR), Beeching, Edward (AUTHOR), Craft, Sam (AUTHOR), Di Forti, Marta (AUTHOR), Frison, Giampietro (AUTHOR), Lindholst, Christian (AUTHOR), Oomen, Pieter E. (AUTHOR), Potter, David (AUTHOR), Rigter, Sander (AUTHOR), Rømer Thomsen, Kristine (AUTHOR), Zamengo, Luca (AUTHOR), Cunningham, Andrew (AUTHOR), Groshkova, Teodora (AUTHOR), Sedefov, Roumen (AUTHOR)
Source: European Archives of Psychiatry & Clinical Neuroscience. Mar2025, Vol. 275 Issue 2, p421-429. 9p.
Subjects: Machine learning, Tetrahydrocannabinol, Drug monitoring, Hashish, Cannabinoids
Geographic Terms: Europe
Abstract: In Europe, concentrations of ∆9-tetrahydrocannabinol (THC) in cannabis resin (also known as hash) have risen markedly in the past decade, potentially increasing risks of mental health disorders. Current approaches to international drug monitoring cannot distinguish between different types of cannabis resin which may have contrasting health effects due to THC and cannabidiol (CBD) content. Here, we compared concentrations of THC and CBD in different types of cannabis resin collected in Europe (either Moroccan-type, or Dutch-type). We then tested the ability of machine learning algorithms to classify the type of cannabis resin (either Moroccan-type, or Dutch-type) using routinely collected monitoring data on THC and CBD. Finally, we applied the optimal algorithm to new samples collected in countries where the type of cannabis resin was unknown, the UK and Denmark. Results showed that overall, Dutch-type samples had higher THC (Hedges' g = 2.39) and lower CBD (Hedges' g = 0.81) than Moroccan-type samples. A Support Vector Machine algorithm achieved classification accuracy exceeding 95%, with little variation in this estimate, good interpretability, and plausibility. It made contrasting predictions about the type of cannabis resin collected in the UK (94% Moroccan-type; 6% Dutch-type) and Denmark (36% Moroccan-type; 64% Dutch-type). In conclusion, we provide proof-of-concept evidence for the potential of machine learning to inform international drug monitoring. Our findings should not be interpreted as objective confirmatory evidence but suggest that Dutch-type cannabis resin has higher THC concentrations than Moroccan-type cannabis resin, which may contribute to variation in drug markets and health outcomes for people who use cannabis in Europe. [ABSTRACT FROM AUTHOR]
Copyright of European Archives of Psychiatry & Clinical Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Applying machine learning to international drug monitoring: classifying cannabis resin collected in Europe using cannabinoid concentrations.
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  Data: <searchLink fieldCode="JN" term="%22European+Archives+of+Psychiatry+%26+Clinical+Neuroscience%22">European Archives of Psychiatry & Clinical Neuroscience</searchLink>. Mar2025, Vol. 275 Issue 2, p421-429. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Tetrahydrocannabinol%22">Tetrahydrocannabinol</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+monitoring%22">Drug monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Hashish%22">Hashish</searchLink><br /><searchLink fieldCode="DE" term="%22Cannabinoids%22">Cannabinoids</searchLink>
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  Data: In Europe, concentrations of ∆9-tetrahydrocannabinol (THC) in cannabis resin (also known as hash) have risen markedly in the past decade, potentially increasing risks of mental health disorders. Current approaches to international drug monitoring cannot distinguish between different types of cannabis resin which may have contrasting health effects due to THC and cannabidiol (CBD) content. Here, we compared concentrations of THC and CBD in different types of cannabis resin collected in Europe (either Moroccan-type, or Dutch-type). We then tested the ability of machine learning algorithms to classify the type of cannabis resin (either Moroccan-type, or Dutch-type) using routinely collected monitoring data on THC and CBD. Finally, we applied the optimal algorithm to new samples collected in countries where the type of cannabis resin was unknown, the UK and Denmark. Results showed that overall, Dutch-type samples had higher THC (Hedges' g = 2.39) and lower CBD (Hedges' g = 0.81) than Moroccan-type samples. A Support Vector Machine algorithm achieved classification accuracy exceeding 95%, with little variation in this estimate, good interpretability, and plausibility. It made contrasting predictions about the type of cannabis resin collected in the UK (94% Moroccan-type; 6% Dutch-type) and Denmark (36% Moroccan-type; 64% Dutch-type). In conclusion, we provide proof-of-concept evidence for the potential of machine learning to inform international drug monitoring. Our findings should not be interpreted as objective confirmatory evidence but suggest that Dutch-type cannabis resin has higher THC concentrations than Moroccan-type cannabis resin, which may contribute to variation in drug markets and health outcomes for people who use cannabis in Europe. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Archives of Psychiatry & Clinical Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00406-024-01816-w
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 421
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Tetrahydrocannabinol
        Type: general
      – SubjectFull: Drug monitoring
        Type: general
      – SubjectFull: Hashish
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      – SubjectFull: Cannabinoids
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      – SubjectFull: Europe
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              Text: Mar2025
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