A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD

被引:0
作者
Komijani, Saeedeh [1 ]
Ghosal, Dipak [1 ]
Singh, Manpreet K. [2 ]
Schweitzer, Julie B. [2 ,3 ]
Mukherjee, Prerona [2 ,3 ]
机构
[1] Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 USA
[2] Univ Calif Davis, Dept Psychiat & Behav Sci, Davis, CA USA
[3] Univ Calif Davis, MIND Inst, Davis, CA USA
基金
美国国家卫生研究院;
关键词
ADHD; irritability; adolescents; young adults; symptom prediction; hierarchical clustering; machine learning; random forest; INTERNATIONAL NEUROPSYCHIATRIC INTERVIEW; CHILDREN; COMORBIDITY; DISORDER; PSYCHOPATHOLOGY; ASSOCIATIONS; TEMPERAMENT; RELIABILITY; IMPACT; YOUTH;
D O I
10.3389/fpsyt.2024.1467486
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Background Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children and adolescents characterized by persistent patterns of hyperactivity, impulsivity, and inattentiveness. ADHD persists for many into adulthood. While irritability is not a diagnostic symptom of ADHD, temper outbursts and irritable moods are common in individuals with ADHD. However, research on the association between irritability and ADHD symptoms in adolescents and young adults remains limited. Method Prior research has used linear regression models to examine longitudinal relations between ADHD and irritability symptoms. This method may be impacted by the potential presence of highly colinear variables. We utilized a hierarchical clustering technique to mitigate these collinearity issues and implemented a non-parametric machine learning (ML) model to predict the significance of symptom relations over time. Our data included adolescents (N=148, 54% ADHD) and young adults (N=124, 42% ADHD) diagnosed with ADHD and neurotypical (NT) individuals, evaluated in a longitudinal study. Results Results from the linear regression analysis indicate a significant association between irritability at time-point 1 (T1) and hyperactive-impulsive symptoms at time-point 2 (T2) in adolescent females (beta=0.26, p-value < 0.001), and inattentiveness at T1 with irritability at T2 in young adult females (beta=0.49, p-value < 0.05). Using a non-parametric-based approach, employing the Random Forest (RF) method, we found that among both adolescents and young adults, irritability in adolescent females significantly contributes to predicting impulsive symptoms in subsequent years, achieving a performance rate of 86%. Conclusion Our results corroborate and extend prior findings, allowing for an in-depth examination of longitudinal relations between irritability and ADHD symptoms, namely hyperactivity, impulsivity, and inattentiveness, and the unique association between irritability and ADHD symptoms in females.
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页数:14
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