What question did this study set out to answer?

The research aims to identify a multidimensional signature that predicts relapse and long-term outcomes in alcohol dependence treatment using machine learning.

April 21, 2026SHILAP Revista de lepidopterologíaOpen Access

The patterns of relapse and abstinence: using machine learning to identify a multidimensional signature of long-term outcome after inpatient alcohol withdrawal treatment

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Authors

FRFlorian J. Raabe SBSarah BrechtelLudwig-Maximilians-Universität München CLCharlotte LugmairMax Planck Institute of Psychiatry

Discussion

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Overview

Observational study reveals relationships between clinical features and alcohol treatment outcomes, suggesting multifaceted treatment approaches.

Key Points

The research aims to identify a multidimensional signature that predicts relapse and long-term outcomes in alcohol dependence treatment using machine learning.
Inpatients with alcohol dependence received detoxification and cognitive behavioral therapy.
Participants were followed up six months post-treatment to assess their abstinence and drinking behavior.
Cross-validated sparse partial least squares analysis was used to link clinical features with four long-term outcomes.
46% of participants achieved continuous abstinence six months after treatment.
Key factors linked to positive outcomes included higher education, employment, and completion of withdrawal treatment.
Negative outcomes were associated with alcohol-related health damage, repeated withdrawal treatments, and living in shelters.

Cite This Study

Raabe et al. (2026) studied this question.

synapsesocial.com/papers/69e7132bcb99343efc98cf42 https://doi.org/10.3389/fpsyt.2026.1683069

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April 21, 2026SHILAP Revista de lepidopterologíaOpen Access

The patterns of relapse and abstinence: using machine learning to identify a multidimensional signature of long-term outcome after inpatient alcohol withdrawal treatment

View Full Paper

Ask AI

Bookmark

Authors

FRFlorian J. Raabe SBSarah BrechtelLudwig-Maximilians-Universität München CLCharlotte LugmairMax Planck Institute of Psychiatry

Discussion

Member takes

Overview

Observational study reveals relationships between clinical features and alcohol treatment outcomes, suggesting multifaceted treatment approaches.

Key Points

The research aims to identify a multidimensional signature that predicts relapse and long-term outcomes in alcohol dependence treatment using machine learning.
Inpatients with alcohol dependence received detoxification and cognitive behavioral therapy.
Participants were followed up six months post-treatment to assess their abstinence and drinking behavior.
Cross-validated sparse partial least squares analysis was used to link clinical features with four long-term outcomes.
46% of participants achieved continuous abstinence six months after treatment.
Key factors linked to positive outcomes included higher education, employment, and completion of withdrawal treatment.
Negative outcomes were associated with alcohol-related health damage, repeated withdrawal treatments, and living in shelters.

Cite This Study

Raabe et al. (2026) studied this question.

synapsesocial.com/papers/69e7132bcb99343efc98cf42 https://doi.org/10.3389/fpsyt.2026.1683069

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