EligibleTrials

NLP Analysis of Weekly Narratives for Dynamic Clinical Assessment in SUD

Recruiting · NCT07595614 · Observational (researchers observe without assigning treatment) · Lead sponsor: Lauro Gutiérrez Castro

View the official record on ClinicalTrials.gov →
Anxiety

Interventions studied

Residential rehabilitation as usual

What this trial is about

This prospective observational study follows adults undergoing residential rehabilitation for severe substance use disorders at a specialized treatment center in Mexico. Participants provide weekly written narratives describing their emotions, challenges, coping strategies, and treatment experiences, and complete validated psychological questionnaires every two weeks, including the Generalized Anxiety Disorder-7 (GAD-7), Environmental Reward Observation Scale (EROS), Automatic Thoughts Questionnaire-8 (ATQ-8), and Behavioral Activation for Depression Scale (BADS). The study applies natural language processing (NLP) and machine learning methods to analyze participants' narratives and identify emotional, cognitive, and behavioral patterns associated with clinical change over time. Narrative-derived features are combined with questionnaire scores to generate a dynamic clinical risk representation that may help detect early signs of psychological worsening or improvement during residential treatment. Participants continue receiving standard residential care, and the study does not modify treatment decisions or clinical interventions. Up to 35 participants with sufficient longitudinal follow-up data will be included in the primary analysis. Data collection is expected to continue through September 2026.

Who can take part

Age range
18 Years and older
Sex
Male
Healthy volunteers
No - a diagnosis or condition is required
Phase
Not specified
Study type
Observational (researchers observe without assigning treatment)

Inclusion criteria

Exclusion criteria

Where it is running

1 location listed.

Read the full protocol, contacts and eligibility on ClinicalTrials.gov →

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