{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T22:24:32Z","timestamp":1788906272482,"version":"build-2803163510"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>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\u2019s alignment with DSM-5.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2019s inclusion of non-ADHD-items slightly reduced subtype specificity. M-CHAT validation further confirmed the designed pipeline\u2019s ability to exclude non-ADHD symptoms, supporting its classification precision.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2025.1671747","type":"journal-article","created":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T14:54:27Z","timestamp":1769439267000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A multi-layer similarity approach for analyzing ADHD symptomology and assessment methods considering DSM-5 diagnostic 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