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Cross-sectional Functional Stratification Based on Psychometric Profiling and Machine Learning in Patients With Substance Use Disorders (SUD)

NCT07590154 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 155

Last updated 2026-05-18

No results posted yet for this study

Summary

Substance use disorders (SUDs) show considerable clinical heterogeneity that limits the usefulness of traditional categorical diagnoses. This observational, cross-sectional study aims to apply an unsupervised deep learning method - an autoencoder - to learn continuous latent representations from standardised psychometric data and to explore whether those representations can help stratify clinical subpopulations. The investigators will recruit 155 adults undergoing residential treatment for SUD. Participants will complete six validated instruments assessing impulsivity (BIS-11), anger regulation (STAXI-2), behavioural activation/avoidance (BADS), borderline symptomatology (BSL-23), generalised anxiety (GAD-7), and environmental reward (EROS). Demographic and clinical variables (age, sex, primary substance, years of use, prior treatments) will also be recorded.

After data cleaning and standardisation (z-scores), a symmetric autoencoder with a 12-dimensional bottleneck (architecture 21-32-24-12-24-32-21) will be trained using mean squared error loss. Regularisation includes L2 weight decay and dropout. The model will be trained 30 times with different random seeds to assess stability; the five best models (by validation pseudo-R²) will be combined into a weighted ensemble. Five-fold cross-validation will evaluate generalisation. For comparison, principal component analysis (PCA) will be applied to the same data. Gaussian mixture models (GMM) will be fitted on the latent space to explore potential clinical subgroups.

The primary outcome is the stability of the latent representation (coefficient of variation of validation MSE across runs). Secondary outcomes include reconstruction performance (pseudo-R²) of the ensemble, comparison with PCA, and the interpretability of latent dimensions via correlations with original variables. GMM results will be described using BIC, silhouette width, bootstrap stability, and clinical characterisation of clusters.

This study does not involve any intervention. Results will be hypothesis-generating and require external validation. No automated clinical decisions will be made.

Conditions

  • Substance Abuse Disorder
  • Addiction
  • Clinical Stratification
  • Substance-related Disorders
  • Residential Treatment Program

Interventions

OTHER

No intervention (observational only)

This is a purely observational study. No drug, device, behavioral therapy, or other intervention was assigned. The study only involved standardized psychometric measurements.

Sponsors & Collaborators

  • Lauro Gutiérrez Castro

    lead OTHER

Principal Investigators

  • Lauro Gutiérrez Castro · Under The Tree

Eligibility

Min Age
18 Years
Max Age
60 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-03-25
Primary Completion
2026-02-18
Completion
2026-04-22

Countries

  • Mexico

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07590154 on ClinicalTrials.gov