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Related Experiment Video

Updated: May 30, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

Published on: March 12, 2020

Detecting noncredible symptomology in ADHD evaluations using machine learning.

John-Christopher A Finley¹, Matthew S Phillips², Jason R Soble^2,3

¹Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Journal of Clinical and Experimental Neuropsychology

|January 25, 2025

Summary

This summary is machine-generated.

Unsupervised machine learning (ML) can effectively detect noncredible symptom reporting in adults undergoing attention-deficit/hyperactivity disorder (ADHD) evaluations. This novel method aids in improving diagnostic accuracy by identifying exaggerated or fabricated symptoms.

Keywords:

ADHD Machine learning artificial intelligence malinger symptom validity

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Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD

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Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Published on: April 1, 2018

Area of Science:

Psychiatry and Psychology
Artificial Intelligence in Healthcare
Clinical Assessment

Background:

Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are increasingly complicated by symptom fabrication or exaggeration.
Novel methods are needed to improve the detection of noncredible symptoms in adult ADHD assessments.

Purpose of the Study:

To investigate the utility of unsupervised machine learning (ML) in detecting noncredible symptom reporting during adult ADHD evaluations.
To assess if ML can identify individuals who fabricate or exaggerate ADHD symptoms.

Main Methods:

An unsupervised ML model (sidClustering) was applied to symptom validity test scores from 623 adults undergoing ADHD evaluations.
The model synthesized raw scores from self-report questionnaires without using predetermined cutoffs.
ML-derived groups were compared against established ratings of credible versus noncredible symptom reporting.

Main Results:

The ML model successfully identified two distinct groups significantly associated with credible and noncredible symptom reporting.
The model's performance was consistent regardless of the number of validity test elevations used to define noncredible reporting.
Validity tests for general psychiatric symptoms were most influential, followed by ADHD-specific symptom validity tests.

Conclusions:

Unsupervised ML can effectively identify noncredible symptom reporting in ADHD evaluations using symptom validity test scores without cutoffs.
The findings support the use of two validity test elevations for identifying noncredible reporting.
Unsupervised ML shows promise as a supplementary tool for enhancing the accuracy and efficiency of ADHD diagnostic evaluations.