ArticleTranslational psychiatry2021
Predicting alcohol use disorder remission: a longitudinal multimodal multi-featured machine learning approach.
Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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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
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Leveraging pretrained vision transformers for classifying alcohol use disorder using raw resting-state EEG.Neuroimage. Reports · 2026Article
- Investigating Factors Associated With Spontaneous Remission in Individuals With Alcohol Use Disorder-Results From a Multi-Site Longitudinal Cohort Study.Addiction biology · 2026Observational
- Alcohol use disorder is associated with altered frontomedial phase-amplitude coupling strength during resting state.Neuroimage. Reports · 2026Article
- Leveraging Pretrained Vision Transformers for classifying Alcohol Use Disorder using Raw Resting-State EEG.bioRxiv : the preprint server for biology · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- The patterns of relapse and abstinence: using machine learning to identify a multidimensional signature of long-term outcome after inpatient alcohol withdrawal treatment.Frontiers in psychiatry · 2026Article
- The effects of polygenic risks for alcohol misuse on negative emotion processing in young adult binge drinkers.Translational psychiatry · 2025Article
- Alzheimer's subtypes A supervised, unsupervised, multimodal, multilayered embedded recursive (SUMMER) AI study.bioRxiv : the preprint server for biology · 2025Article
- Integrating multiple data sources to predict all-cause readmission or mortality in patients with substance misuse.PLOS digital health · 2025Article
- A CNN-Transformer Fusion Model for Proactive Detection of Schizophrenia Relapse from EEG Signals.Bioengineering (Basel, Switzerland) · 2025Article
- Predictors of treatment attrition among individuals in substance use disorder treatment: A machine learning approach.Addictive behaviors · 2025Article
- Next-generation biomarkers for alcohol consumption and alcohol use disorder diagnosis, prognosis, and treatment: A critical review.Alcohol, clinical & experimental research · 2025Review
- Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders.The Journal of clinical investigation · 2024Review
- Drinking behavior patterns may be associated with persistent depressive symptoms after alcohol abstinence in alcohol use disorder.Neuropsychopharmacology reports · 2024Article
- Disentangling heterogeneity in substance use disorder: Insights from genome-wide polygenic scores.Translational psychiatry · 2024Article
- Associations between alcohol use disorder polygenic score and remission in participants from high-risk families and the Indiana Biobank.Alcohol, clinical & experimental research · 2024Article
- The collaborative study on the genetics of alcoholism: Brain function.Genes, brain, and behavior · 2023Review
- The Collaborative Study on the Genetics of Alcoholism: Overview.Genes, brain, and behavior · 2023Review
- The collaborative study on the genetics of alcoholism: Sample and clinical data.Genes, brain, and behavior · 2023Review
Corrections and comments
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Authors and funding
24 authors at 7 institutions in 2 countries.
Funding
Abstract
Predictive models for recovering from alcohol use disorder (AUD) and identifying related predisposition biomarkers can have a tremendous impact on addiction treatment outcomes and cost reduction. Our sample (N = 1376) included individuals of European (EA) and African (AA) ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA) who were initially assessed as having AUD (DSM-5) and reassessed years later as either having AUD or in remission. To predict this difference in AUD recovery status, we analyzed the initial data using multimodal, multi-features machine learning applications including EEG source-level functional brain connectivity, Polygenic Risk Scores (PRS), medications, and demographic information. Sex and ancestry age-matched stratified analyses were performed with supervised linear Support Vector Machine application and were calculated twice, once when the ancestry was defined by self-report and once defined by genetic data. Multifeatured prediction models achieved higher accuracy scores than models based on a single domain and higher scores in male models when the ancestry was based on genetic data. The AA male group model with PRS, EEG functional connectivity, marital and employment status features achieved the highest accuracy of 86.04%. Several discriminative features were identified, including collections of PRS related to neuroticism, depression, aggression, years of education, and alcohol consumption phenotypes. Other discriminated features included being married, employed, medication, lower default mode network and fusiform connectivity, and higher insula connectivity. Results highlight the importance of increasing genetic homogeneity of analyzed groups, identifying sex, and ancestry-specific features to increase prediction scores revealing biomarkers related to AUD remission.
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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.