Evidence map›Paper›PMID 41413208›Full record

ArticleScientific reports2025

Using explainable machine learning to elucidate social and neurobehavioral risk factors linked to nonmedicinal opioid use in young adults.

Andrea M Maxwell, Gregory R Niklason, Sisi Ma, Anna Zilverstand

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Andrea M Maxwell *Department of Psychiatry and Behavioral Sciences, University of Minnesota, 717 Delaware St. SE, Minneapolis, MN, 55414, USA.
Gregory R Niklason *Department of Psychiatry and Behavioral Sciences, University of Minnesota, 717 Delaware St. SE, Minneapolis, MN, 55414, USA.
Sisi MaInstitute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Anna ZilverstandDepartment of Psychiatry and Behavioral Sciences, University of Minnesota, 717 Delaware St. SE, Minneapolis, MN, 55414, USA. annaz@umn.edu.

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UM1TR004405 · NCATS · UNIVERSITY OF MINNESOTA · PI Bruce R Blazar, Damien A Fair · 2023 to 2026
$30.8M
Personalized relapse prediction in Alcohol Use DisorderR01AA029406 · NIAAA · UNIVERSITY OF MINNESOTA · PI Anna Zilverstand · 2023 to 2026
$2.0M
The role of stress, social support, and brain function on alcohol misuse in womenF30AA030900 · NIAAA · UNIVERSITY OF MINNESOTA · PI Andrea Maxwell · 2023 to 2026
$189k
NCATS NIH HHS UM1 TR004405NIAAA NIH HHS 1R01AA029406NIAAA NIH HHS 5F30AA030900NIAAA NIH HHS F30 AA030900NIAAA NIH HHS R01 AA029406
6 · The paper itself

Abstract

Nonmedicinal opioid use (here defined as > 5 lifetime uses of illicit opioids or nonmedical use of prescription opioids) can be a precursor to Opioid Use Disorder (OUD). The risk factors underlying opioid misuse are likely complex and have not been well-described empirically. Here, we generated an exploratory, highly dimensional model of risk factors for nonmedicinal opioid use. We hypothesized that factors from the triadic model of addiction, which links alterations in reward processing, cognitive function, and negative emotionality to high levels of substance use, will also be important in classifying lower levels of recurrent nonmedicinal opioid use. We combined two state-of-the-art machine learning methods, eXtreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP), and applied them to the available phenotypic data from the Human Connectome Project (HCP) data (N = 1206, ages 22–35, 54% female). By doing so, we identified the phenotypic variables that were most important (highest ranked) for classifying individuals as a self-reported recurrent nonmedicinal opioid users. We found that factors linked to dysregulated reward processing (substance use history), altered cognitive function (working memory, language task), and higher negative emotionality (depressive symptoms) were among the top-ranked predictors of nonmedicinal recurrent opioid use. Additionally, several factors related to dysregulated social function (higher loneliness, altered social cognition) were highly ranked, contributing to the top 50% of the model’s predictive value. These findings provide empirical evidence that the triadic model of addiction indeed applies to nonmedicinal opioid use as a precursor of OUD, suggesting that this model may also be a ‘model of vulnerability’ for addiction. Furthermore, we found novel evidence that altered social function contributed to classification of opioid misuse, emphasizing the need to integrate social function into a model of risk factors.

Indexed as

Analgesics, OpioidMachine LearningOpioid-Related DisordersAdultBoosting Machine Learning AlgorithmsCognitionFemaleHumansMaleRisk FactorsYoung AdultAnalgesics, OpioidCognitionDepressionLonelinessOpioid use disorderRewardSocialWorking memory

Identifiers

PMID41413208
PMCPMC12824202

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.