ArticleNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2023
AI-based analysis of social media language predicts addiction treatment dropout at 90 days.
Article in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled 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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Integrating Psychosocial Factors into Artificial Intelligence Models for Predicting Addiction Treatment Outcomes: A Systematic Review.European addiction research · 2026Pooled it
- Opportunities and risks of large language models in digital interventions for substance use disorders.Current opinion in psychiatry · 2026Review
- Artificial intelligence for alcohol, opioid, and cannabis use disorders screening and management: a narrative review of barriers and facilitators to clinical implementation.Frontiers in digital health · 2026Review
- The Impaired Response Inhibition and Salience Attribution Model of Drug Addiction: Recent Neuroimaging Evidence and Future Directions.Annual review of psychology · 2026Review
- Intersection of Big Five Personality Traits and Substance Use on Social Media Discourse: AI-Powered Observational Study.Journal of medical Internet research · 2025Observational
- Speak and You Shall Predict: Evidence That Speech at Initial Cocaine Abstinence Is a Biomarker of Long-Term Drug Use Behavior.Biological psychiatry · 2025Article
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- Automatic implicit motive codings are at least as accurate as humans' and 99% faster.Journal of personality and social psychology · 2025Article
- Targeting the Neonatal Fc Receptor in Autoimmune Diseases: Pipeline and Progress.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2025Review
- Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?Journal of medical Internet research · 2025Article
- Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.Addiction (Abingdon, England) · 2024Observational
- Artificial Intelligence in Addiction: Challenges and Opportunities.Indian journal of psychological medicine · 2024Article
- Linguistic predictors from Facebook postings of substance use disorder treatment retention versus discontinuation.The American journal of drug and alcohol abuse · 2022Article
- Enhancing mental health care through human-computer interaction: AI holistic design of a mental health APP in China.Digital healthArticle
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
The reoccurrence of use (relapse) and treatment dropout is frequently observed in substance use disorder (SUD) treatment. In the current paper, we evaluated the predictive capability of an AI-based digital phenotype using the social media language of patients receiving treatment for substance use disorders (N = 269). We found that language phenotypes outperformed a standard intake psychometric assessment scale when predicting patients' 90-day treatment outcomes. We also use a modern deep learning-based AI model, Bidirectional Encoder Representations from Transformers (BERT) to generate risk scores using pre-treatment digital phenotype and intake clinic data to predict dropout probabilities. Nearly all individuals labeled as low-risk remained in treatment while those identified as high-risk dropped out (risk score for dropout AUC = 0.81; p < 0.001). The current study suggests the possibility of utilizing social media digital phenotypes as a new tool for intake risk assessment to identify individuals most at risk of treatment dropout and relapse.
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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.