ArticleDrug and alcohol dependence2020
Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder.
Article in Drug and alcohol dependence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 57 citations in OpenAlex.
- The Use of Machine Learning to Predict Offline Adolescent e-Cigarette Use: a Proof-of-Concept.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- A Bayesian Learning Model for Joint Risk Prediction of Alcohol and Cannabis Use Disorders.Statistics in medicine · 2026Article
- Distinguishing a drug use disorder from drug use in a high-risk sample of youth: A random forest classification and explanatory analysis.Drug and alcohol dependence reports · 2026Article
- Predicting substance use behaviors with machine learning using small sets of judgment and contextual variables.Npj mental health research · 2026Article
- Craving for a Robust Methodology: A Systematic Review of Machine Learning Algorithms on Substance-Use Disorders Treatment Outcomes.International journal of mental health and addiction · 2026Article
- Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Youth by Using Mobile Phone Data: Development and Validation Study.Online journal of public health informatics · 2025Article
- GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.The AAPS journal · 2025Article
- Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.BMJ open quality · 2025Article
- The Role of Despair in Predicting Self-Destructive Behaviors.Population research and policy review · 2025Article
- Spatial Craving Patterns in Marijuana Users: Insights From fMRI Brain Connectivity Analysis With High-Order Graph Attention Neural Networks.IEEE journal of biomedical and health informatics · 2025Article
- Artificial Intelligence in Addiction: Challenges and Opportunities.Indian journal of psychological medicine · 2024Article
- Risk and Promotive Factors Related to Cannabis Use Among American Indian Adolescents.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Neonatal brain inflammation enhances methamphetamine-induced reinstated behavioral sensitization in adult rats analyzed with explainable machine learning.Neurochemistry international · 2024Article
- Factors Associated with Substance Use Disorder among High-Need Adolescents and Young Adults in North Carolina.North Carolina medical journal · 2024Article
- Analyzing and predicting short-term substance use behaviors of persons who use drugs in the great plains of the U.S.PloS one · 2024Article
- Physical inactivity during adolescence heightens risk for cannabis use disorder in adulthood.Experimental and clinical psychopharmacology · 2023Article
- Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use.Addiction (Abingdon, England) · 2023Article
- Ketamine use disorder: preclinical, clinical, and neuroimaging evidence to support proposed mechanisms of actions.Intelligent medicine · 2022Article
- Locating Youth Exposed to Parental Justice Involvement in the Electronic Health Record: Development of a Natural Language Processing Model.JMIR pediatrics and parenting · 2022Article
- Joint risk prediction for hazardous use of alcohol, cannabis, and tobacco among adolescents: A preliminary study using statistical and machine learning.Preventive medicine reports · 2022Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSubstance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypothesized that ML identifies the health, psychological, psychiatric, and contextual features to predict SUD, and the identified features predict high-risk individuals to develop SUD.
methodMale (N = 494) and female (N = 206) participants and their informant parents were administered a battery of questionnaires across five waves of assessment conducted at 10-12, 12-14, 16, 19, and 22 years of age. Characteristics most strongly associated with SUD were identified using the random forest (RF)algorithm from approximately 1000 variables measured at each assessment. Next, the complement of features was validated, and the best models were selected for predicting SUD using seven ML algorithms. Lastly, area under the receiver operating characteristic curve (AUROC) evaluated accuracy of detecting individuals who develop SUD+/- up to thirty years of age.
resultsApproximately thirty variables strongly predict SUD. The predictors shift from psychological dysregulation and poor health behavior in late childhood to non-normative socialization in mid to late adolescence. In 10-12-year-old youths, the features predict SUD+/- with 74% accuracy, increasing to 86% at 22 years of age. The RF algorithm optimally detects individuals between 10-22 years of age who develop SUD compared to other ML algorithms.
conclusionThese findings inform the items required for inclusion in instruments to accurately identify high risk youths and young adults requiring SUD prevention.
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Registered trials
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.