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Related Concept Videos

Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...

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Related Experiment Video

Updated: Jul 12, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Using Machine Learning with the Brief Symptom Inventory to Screen for Comorbid Addictions: A Proof-of-Principle

Maor Daniel Levitin, Dvora Shmulewitz, Roi Eliashar

    European Addiction Research
    |July 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    The Brief Symptom Inventory (BSI) can predict addiction risk in mental health patients. Machine learning models using BSI data show potential for identifying individuals needing addiction screening and interventions.

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    Last Updated: Jul 12, 2026

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
    07:31

    Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

    Published on: May 15, 2020

    Area of Science:

    • Psychiatry and Behavioral Sciences
    • Computational Psychiatry
    • Addiction Medicine

    Background:

    • Mental health disorders frequently co-occur with substance use and other addictive behaviors.
    • Current mental health care often lacks screening for these co-occurring addictions.
    • General psychopathology measures, like the BSI, may indicate addiction risk.

    Purpose of the Study:

    • To investigate the predictive utility of the Brief Symptom Inventory (BSI) for problematic substance use and behavioral addictions.
    • To explore the application of machine learning models in identifying addiction risk from BSI responses.
    • To assess the potential of leveraging existing psychopathology measures for addiction screening.

    Main Methods:

    • A population sample (N=2,451) of Jewish adults in Israel completed the BSI and assessments for substance use and behavioral addictions (gambling, gaming, hypersexual behavior, pornography use).
    • Various machine learning models (decision trees, random forest, boosting, LASSO, subset-selection, elastic net regression) were utilized.
    • Models predicted addiction outcomes based on BSI responses.

    Main Results:

    • The BSI showed predictive ability for various addictions, with explained variance (R2) ranging from 2.9% to 27.0%.
    • Classification capabilities (AUC) ranged from 0.60 to 0.84, indicating moderate predictive power.
    • Behavioral addictions demonstrated generally higher predictability than substance use disorders.

    Conclusions:

    • The BSI contains significant information regarding addiction risk, supporting its use in screening for comorbid addictions in mental health settings.
    • This study serves as proof of principle that existing psychopathology measures can inform about potential comorbidities.
    • Efficient identification of at-risk mental health patients can optimize intervention strategies.