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Empirically derived symptom profiles in adults with attention-Deficit/hyperactivity disorder: An unsupervised machine learning approach.

Authors :
Rodriguez VJ
Finley JA
Liu Q
Alfonso D
Basurto KS
Oh A
Nili A
Paltell KC
Hoots JK
Ovsiew GP
Resch ZJ
Ulrich DM
Soble JR
Source :
Applied neuropsychology. Adult [Appl Neuropsychol Adult] 2024 Apr 24, pp. 1-10. Date of Electronic Publication: 2024 Apr 24.
Publication Year :
2024
Publisher :
Ahead of Print

Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) is associated with various cognitive, behavioral, and mood symptoms that complicate diagnosis and treatment. The heterogeneity of these symptoms may also vary depending on certain sociodemographic factors. It is therefore important to establish more homogenous symptom profiles in patients with ADHD and determine their association with the patient's sociodemographic makeup. The current study used unsupervised machine learning to identify symptom profiles across various cognitive, behavioral, and mood symptoms in adults with ADHD. It was then examined whether symptom profiles differed based on relevant sociodemographic factors.<br />Methods: Participants were 382 adult outpatients (62% female; 51% non-Hispanic White) referred for neuropsychological evaluation for ADHD.<br />Results: Employing Gaussian Mixture Modeling, we identified two distinct symptom profiles in adults with ADHD: "ADHD-Plus Symptom Profile" and "ADHD-Predominate Symptom Profile." These profiles were primarily differentiated by internalizing psychopathology (Cohen's d  = 1.94-2.05), rather than by subjective behavioral and cognitive symptoms of ADHD or neurocognitive test performance. In a subset of 126 adults without ADHD who were referred for the same evaluation, the unsupervised machine learning algorithm only identified one symptom profile. Group comparison analyses indicated that female patients were most likely to present with an ADHD-Plus Symptom Profile ( χ2  = 5.43, p < .001).<br />Conclusion: The machine learning technique used in this study appears to be an effective way to elucidate symptom profiles emerging from comprehensive ADHD evaluations. These findings further underscore the importance of considering internalizing symptoms and patients' sex when contextualizing adult ADHD diagnosis and treatment.

Details

Language :
English
ISSN :
2327-9109
Database :
MEDLINE
Journal :
Applied neuropsychology. Adult
Publication Type :
Academic Journal
Accession number :
38657158
Full Text :
https://doi.org/10.1080/23279095.2024.2343022