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

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.

Methods

Participants were 382 adult outpatients (62% female; 51% non-Hispanic White) referred for neuropsychological evaluation for ADHD.

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).

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.

Keywords:

• ADHD
• neuropsychology
• internalizing symptoms
• machine learning
• symptom profile

Acknowledgement

Violeta J. Rodriguez’s work on this study was partially supported by a grant from R36ActfMH127838. The funding agencies did not participate in the study design, data collection, analysis, decision to publish, or preparation of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Transparency statement

The current study was not registered. Given that this study was based on patient data, data will not be made publicly available.

Additional information

Funding

This work was supported by the Foundation for the National Institutes of Health.