Article

Integrating Genetic, Environmental, Cognitive, and Temperament Data for ADHD Prediction in Explainable Deep Learning Models

2026-07-01

Abstract excerpt

<h4> Abstract </h4> <h4>Objective</h4> Attention-deficit/hyperactivity disorder (ADHD) is clinically and etiologically heterogeneous, and diagnostic decisions may benefit from integrating multiple sources of information. We developed an explainable deep learning approach to test whether genetic, environmental, cognitive, demographic, and temperament data could classify ADHD diagnosis and identify features contr...

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Literature Corpus work: 0058aac2-e556-55b0-a544-2a6764a0f5ae
DOI: 10.64898/2026.06.29.26356796

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