Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Hot Topics
  • Published:

Leveraging AI to predict substance use disorder treatment outcomes

Neuropsychopharmacology volume 49, pages 335–336 (2024)Cite this article

Subjects

The predominant challenge in the management of substance use disorders (SUD) lies in the frequent recurrence of use (relapse) and patient departure from treatment programs (dropout). These situations, more the rule than the exception, have been traditionally examined through comprehensive assessments of various factors we believe contribute to relapse, such as the severity of addiction, demographic elements, and life history [1]. Such factors are often assessed at treatment entry with structured interviews that are dependent on the patient’s retrospective self-reporting of a highly complex health condition.

We compared the predictive power of this social media language-based digital phenotype with that of the Addiction Severity Index [3], a widely used structured interview, to predict 90-day SUD treatment outcomes (a three-way classification task: abstinence, relapse, and dropout). Results showed that the digital phenotype outperformed the traditional interview method (AUC 0.725 vs. 0.658; Fig. 1B), via an out-of-sample cross-validation setup. This suggests that the digital phenotype, derived from patients’ social media language, could be a powerful tool for identifying patients at high risk for dropout at the beginning of treatment engagement.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study design, Sample Demographics, and Results.

References

  1. McCaul ME, Svikis DS, Moore RD. Predictors of outpatient treatment retention: patient versus substance use characteristics. Drug Alcohol Depend. 2001;62:9–17.

    Article  CAS  PubMed  Google Scholar 

  2. Curtis, B, Giorgi, S, Ungar, L, Vu, H, Yaden, D, Liu, T, et al. AI-based analysis of social media language predicts addiction treatment dropout at 90 days. Neuropsychopharmacology. 2023. https://doi.org/10.1038/s41386-023-01585-5.

  3. McLellan, AT, Alterman, AI, Metzger, DS, Grissom, GR, Woody, GE, Luborsky, L, et al. (1997). Similarity of outcome predictors across opiate, cocaine, and alcohol treatments: role of treatment services. In GA Marlatt & GR VandenBos (Eds.), Addictive behaviors: Readings on etiology, prevention, and treatment (pp. 718–58). (Reprinted from the “Journal of Consulting and Clinical Psychology,” 62, 1994, pp. 1141–1158) American Psychological Association. https://doi.org/10.1037/10248-028

  4. Liu T, Giorgi S, Yadeta K, Schwartz HA, Ungar LH, Curtis B. Linguistic predictors from Facebook postings of substance use disorder treatment retention versus discontinuation. Am J Drug Alcohol Abus. 2022;48:573–85.

    Article  CAS  Google Scholar 

  5. Shipp AJ, Aeon B. Temporal focus: thinking about the past, present, and future. Curr Opin Psychol. 2019;26:37–43.

    Article  PubMed  Google Scholar 

  6. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018;178:1544–7.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This study was funded by the Intramural Research Program of the National Institutes of Health (NIH), National Institute on Drug Abuse (NIDA; ZIA DA000628).

Author information

Authors and Affiliations

  1. Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, USA

    Salvatore Giorgi & Brenda Curtis

Authors
  1. Salvatore Giorgi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brenda Curtis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SG and BC wrote the manuscript and prepared the figures.

Corresponding author

Correspondence to Brenda Curtis.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Giorgi, S., Curtis, B. Leveraging AI to predict substance use disorder treatment outcomes. Neuropsychopharmacol. 49, 335–336 (2024). https://doi.org/10.1038/s41386-023-01700-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41386-023-01700-6

Search

Advanced search

Quick links