Observational Study Explores Deep Learning for Clinical Stratification in Substance Use Disorders — Novel Approach to Personalised Addiction Medicine
247 Health News Editorial
·23 May 2026
·4 min read
·clinical-trialsA completed observational study explores whether deep learning can identify clinically meaningful subgroups within substance use disorders, potentially informing more personalised addiction treatment approaches. The research used autoencoder neural networks to analyse psychological profiles from 155 residential treatment patients.
Originally reported by ClinicalTrials.gov
References
- 1.Gutiérrez Castro, L. (2024). Unsupervised Deep Representation Learning for Clinical Stratification in Substance Use Disorders. ClinicalTrials.gov. NCT07590154. https://clinicaltrials.gov/study/NCT07590154
- 2.Kwako, L. E., Momenan, R., Litten, R. Z., & Koob, G. F. (2016). Addictions neuroclinical assessment: a neuroscience-based framework for addictive disorders. Biological Psychiatry, 80(3), 179-189. https://doi.org/10.1016/j.biopsych.2015.10.024
- 3.McLellan, A. T., Lewis, D. C., O'Brien, C. P., & Kleber, H. D. (2000). Drug dependence, a chronic medical illness: implications for treatment, insurance, and outcomes evaluation. JAMA, 284(13), 1689-1695. https://doi.org/10.1001/jama.284.13.1689
- 4.United Nations Office on Drugs and Crime. (2023). World Drug Report 2023. UNODC. https://www.unodc.org/unodc/en/data-and-analysis/world-drug-report-2023.html
- 5.Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. New England Journal of Medicine, 374(4), 363-371. https://doi.org/10.1056/NEJMra1511480
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247 Health News Editorial
247 Health News is a medical intelligence platform delivering evidence-based research appraisals, clinical trial monitoring, and health technology news for healthcare professionals.
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