Detecting noncredible symptomology in ADHD evaluations using machine learning.

Journal: Journal of clinical and experimental neuropsychology
PMID: 39862143

Abstract

INTRODUCTION: Diagnostic evaluations for attention-deficit/hyperactivity disorder (ADHD) are becoming increasingly complicated by the number of adults who fabricate or exaggerate symptoms. Novel methods are needed to improve the assessment process required to detect these noncredible symptoms. The present study investigated whether unsupervised machine learning (ML) could serve as one such method, and detect noncredible symptom reporting in adults undergoing ADHD evaluations.

Authors

  • John-Christopher A Finley
    Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Matthew S Phillips
    Department of Psychiatry, University of Illinois Chicago College of Medicine, Chicago, IL, USA.
  • Jason R Soble
    Department of Psychiatry, University of Illinois Chicago College of Medicine, Chicago, IL, USA.
  • Violeta J Rodriguez
    Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.

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