Key result
Deep lasso algorithm predicts parent-assessed child inattention from seven family items with ~0.34 correlation.
Why the study?
To introduce the deep lasso algorithm and determine whether it can predict symptom severity in children with ADHD using family burden, family functioning, parental satisfaction, and parental mental health scales.
Can the deep lasso algorithm predict ADHD symptom severity in children using family burden information?
Population
Children with ADHD
Comparison
Deep lasso (dlasso) vs traditional statistical linear lasso
Authors
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May inform brief family-based ADHD screening; hypothesis-generating for machine learning in pediatric psychiatry.
Observational (n=73)
No
Can the deep lasso algorithm predict ADHD symptom severity in children using family burden information?
Absolute Event Rate: 0.34% vs -0.01%
The deep lasso algorithm effectively predicts ADHD severity in children using a small subset of family burden and parental satisfaction items.
Laria et al. (2021) conducted an observational in Attention-deficit hyperactivity disorder (ADHD) (n=73). Deep lasso (dlasso) algorithm vs. Multiple linear regression was evaluated on Correlation between predicted and actual SWAN inattention subscale scores. The deep lasso algorithm predicted parents' assessment of their children's inattention severity using only seven items related to family burden and satisfaction, achieving an average correlation of 0.34.
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