Rodriguez Violeta J, Finley John-Christopher A, Liu Qimin, Alfonso Demy, Basurto Karen S, Oh Alison, Nili Amanda, Paltell Katherine C, Hoots Jennifer K, Ovsiew Gabriel P, Resch Zachary J, Ulrich Devin M, Soble Jason R
Department of Psychology, University of Illinois at Urbana-Champaign, Illinois, USA.
Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Appl Neuropsychol Adult. 2024 Apr 24:1-10. doi: 10.1080/23279095.2024.2343022.
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.
Participants were 382 adult outpatients (62% female; 51% non-Hispanic White) referred for neuropsychological evaluation for ADHD.
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 = 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 ( = 5.43, < .001).
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.
注意力缺陷多动障碍（ADHD）与多种认知、行为和情绪症状相关，这些症状使诊断和治疗变得复杂。这些症状的异质性也可能因某些社会人口统计学因素而有所不同。因此，重要的是在ADHD患者中建立更同质的症状特征，并确定它们与患者社会人口统计学构成的关联。本研究使用无监督机器学习来识别ADHD成人患者各种认知、行为和情绪症状的症状特征。然后检查症状特征是否因相关社会人口统计学因素而有所不同。
参与者为382名因ADHD接受神经心理学评估的成年门诊患者（62%为女性；51%为非西班牙裔白人）。
采用高斯混合模型，我们在ADHD成人患者中识别出两种不同的症状特征：“ADHD附加症状特征”和“ADHD主导症状特征”。这些特征主要通过内化精神病理学来区分（Cohen's = 1.94 - 2.05），而不是通过ADHD的主观行为和认知症状或神经认知测试表现。在126名因相同评估而被转诊的无ADHD的成年人子集中，无监督机器学习算法仅识别出一种症状特征。组间比较分析表明，女性患者最有可能呈现ADHD附加症状特征（ = 5.43， <.001）。
本研究中使用的机器学习技术似乎是阐明综合ADHD评估中出现的症状特征的有效方法。这些发现进一步强调了在成人ADHD诊断和治疗背景下考虑内化症状和患者性别的重要性。
