ArticleFrontiers in psychiatry2025
A multi-layer similarity approach for analyzing ADHD symptomology and assessment methods considering DSM-5 diagnostic criteria.
Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aim: Attention-Deficit-Hyperactivity-Disorder (ADHD) is a neurodevelopmental-condition characterized by two symptom-domains, inattention and hyperactivity/impulsivity, as per DSM-5. Prior research, indicates conceptual-overlap among symptoms within each domain, potentially compromising the diagnostic utility of symptom structure itself. This structural redundancy has direct implications for evaluation of ADHD-screening-tools, which already show substantial heterogeneity in item-content and focus. While full psychometric-validation is resource-intensive, assessing tool alignment with DSM-5 offers a more practical and clinically relevant alternative. Method: Considering these challenges, this study first employed a three-layer-similarity-framework with entropy-based-weighted-combined-score, to investigate intra-domain symptom redundancy. Subsequently, a multi-stage-classification-pipeline, comprising a filtering-layer and machine-learning-classifiers (Random-Forest, Support-Vector-Machine and Logistic-Regression), was trained on DSM-5 ADHD and Non-ADHD (Conduct-Disorder, Major-Depressive-Disorder, Oppositional-Defiant-Disorder) statements, tested on Vanderbilt-preschool-assessment-questionnaire and validated on ADHD-Rating-Scale, Swanson-Nolan-and-Pelham-Rating-Scale (SNAP-IV) and Modified-Checklist-for-Autism-in-Toddlers (M-CHAT), to assess screening-tool's alignment with DSM-5. Results: The results revealed moderate-overlap between symptom-pairs (2 and 5) and (5 and 7) within the inattention-domain, with similarity-scores of 0.62 and 0.58 respectively. The filtering-layer demonstrated high accuracy of 97%, perfect precision and specificity in isolating ADHD symptoms. Among classifiers, Random-Forest achieved the best performance with 92% accuracy, 83% precision, 100% recall and 91% F1-score. Validation with ADHD-Rating-Scale ensured near-perfect classification due to its focused symptom set, while SNAP-IV's inclusion of non-ADHD-items slightly reduced subtype specificity. M-CHAT validation further confirmed the designed pipeline's ability to exclude non-ADHD symptoms, supporting its classification precision. Conclusion: The proposed pipeline can be adopted for analyzing strength and limitations of screening-tools, which serve as a catalyst for refinements, ensuring reliability and effectiveness in practical applications.
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