ArticleHealthcare (Basel, Switzerland)2022
Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Integrating Psychosocial Factors into Artificial Intelligence Models for Predicting Addiction Treatment Outcomes: A Systematic Review.European addiction research · 2026Pooled it
- Opioid agonist treatment outcomes among individuals with a history of nonfatal overdose: Findings from a pragmatic, pan-Canadian, randomized control trial.The American journal on addictions · 2025Trial
- 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
- Predicting treatment retention in medication for opioid use disorder: a machine learning approach using NLP and LLM-derived clinical features.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.Journal of substance use and addiction treatment · 2025Observational
- Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?Journal of medical Internet research · 2025Article
- Predictors of treatment attrition among individuals in substance use disorder treatment: A machine learning approach.Addictive behaviors · 2025Article
- Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.Addiction (Abingdon, England) · 2024Observational
- Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts.Findings of ACL. NAACL · 2024Article
- A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program.
methodsWe have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning approach to predict the individualized risk for dropping out of the program.
resultsOur model achieved sensitivity of 0.81 and specificity of 0.65 for dropout at 90 days and improved the performance to sensitivity of 0.86 and specificity of 0.66 for 120 days. Additionally, we identified individual risk factors for dropout, including previous overdose and relapse and improvement in reported quality of life.
conclusionsOur informatics approach provides insight into an area where programs may allocate additional resources in order to retain high-risk individuals and increase the chances of success in recovery.
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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.