BibTeX for a paper by David Kotz at Dartmouth College.
For more information about this paper, visit this web page:
https://kotz.cs.dartmouth.edu/research/heinz-ema/index.html

@Article{heinz:ema,
  author =        {Michael V. Heinz and George D. Price and Avijit Singh and Sukanya Bhattacharya and Ching-Hua Chen and Asma Asyyed and Monique B. Does and Saeed Hassanpour and Emily Hichborn and David Kotz and Chantal A. Lambert-Harris and Zhiguo Li and Bethany McLeman and Varun Mishra and Catherine Stanger and Geetha Subramaniam and Weiyi Wu and Cynthia I. Campbell and Lisa A. Marsch and Nicholas C. Jacobson},
  title =         {{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 =       {Journal of Substance Use and Addiction Treatment (JSAT)},
  year =          2025,
  month =         {March},
  volume =        173,
  articleno =     209685,
  numpages =      10,
  publisher =     {Elsevier},
  copyright =     {the authors},
  DOI =           {10.1016/j.josat.2025.209685},
  URL =           {https://kotz.cs.dartmouth.edu/research/heinz-ema/index.html},
  abstract =      { \emph{Background:} Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks of adverse outcomes, including death. Predicting important outcomes, including non-prescribed opioid use (NPOU) and treatment discontinuation among persons receiving medications for OUD (MOUD) can provide a proactive approach to these challenges. Our study uses ecological momentary assessment (EMA) and deep learning to predict momentary NPOU, medication nonadherence, and treatment retention in MOUD patients.  \par  \emph{Methods:} Study participants included adults receiving MOUD at a large outpatient treatment program. We predicted NPOU (EMA-based), medication nonadherence (Electronic Health Record [EHR]- and EMA-based), and treatment retention (EHR-based) using context-sensitive EMAs (e.g., stress, pain, social setting). We used recurrent deep learning models with 7-day sliding windows to predict the next-day outcomes, using Area Under the ROC Curve (AUC) for assessment. We employed SHapley additive ExPlanations (SHAP) to understand feature latency and importance.  \par  \emph{Results:} Participants comprised 62 adults with 14,322 observations. Model performance varied across EMA subtypes and outcomes with AUCs spanning 0.59-0.97. Recent substance use was the best performing predictor for EMA-based NPOU (AUC{$=$}0.97) and medication nonadherence (AUC{$=$}0.68); life-contextual factors performed best for EHR-based medication nonadherence (AUC{$=$}0.89) and retention (AUC{$=$}0.80). SHAP revealed varying latencies between predictors and outcomes.  \par  \emph{Conclusions:} Findings support the effectiveness of EMA and deep learning for forecasting actionable outcomes in persons receiving MOUD. These insights will enable the development of personalized dynamic risk profiles and just-in-time adaptive interventions (JITAIs) to mitigate high-risk OUD outcomes. },
}