Leveraging AI to predict substance use disorder treatment outcomes
Author:
Funder
Intramural Research Program of the National Institutes of Health
Publisher
Springer Science and Business Media LLC
Subject
Psychiatry and Mental health,Pharmacology
Link
https://www.nature.com/articles/s41386-023-01700-6.pdf
Reference6 articles.
1. McCaul ME, Svikis DS, Moore RD. Predictors of outpatient treatment retention: patient versus substance use characteristics. Drug Alcohol Depend. 2001;62:9–17.
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.
5. Shipp AJ, Aeon B. Temporal focus: thinking about the past, present, and future. Curr Opin Psychol. 2019;26:37–43.
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